{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "4655e6db-0a5a-48cc-8100-7f7e281c68fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "533750d7-2ed5-414f-9b5e-f256c246c991",
   "metadata": {},
   "source": [
    "# COST COMPARISON (VARYING VM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "a56c6508-e700-4cf1-b33a-a0cf3d38ccaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cost_vm=pd.read_excel(\"cost vs vm 2.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "c0d3c9d0-3f66-451b-84aa-7b50c598e609",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No of VMs</th>\n",
       "      <th>RR</th>\n",
       "      <th>SJF</th>\n",
       "      <th>FWA</th>\n",
       "      <th>SQSA</th>\n",
       "      <th>BAT</th>\n",
       "      <th>PSO</th>\n",
       "      <th>BMO</th>\n",
       "      <th>SSA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>326.134301</td>\n",
       "      <td>325.055427</td>\n",
       "      <td>305.999589</td>\n",
       "      <td>308.243038</td>\n",
       "      <td>313.967940</td>\n",
       "      <td>316.306597</td>\n",
       "      <td>308.527742</td>\n",
       "      <td>309.106937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15</td>\n",
       "      <td>469.203217</td>\n",
       "      <td>470.027151</td>\n",
       "      <td>440.749030</td>\n",
       "      <td>442.265115</td>\n",
       "      <td>448.529936</td>\n",
       "      <td>451.757597</td>\n",
       "      <td>444.996194</td>\n",
       "      <td>438.609238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>521.887581</td>\n",
       "      <td>537.192201</td>\n",
       "      <td>471.518600</td>\n",
       "      <td>470.558280</td>\n",
       "      <td>477.285336</td>\n",
       "      <td>492.576291</td>\n",
       "      <td>483.640668</td>\n",
       "      <td>469.892662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25</td>\n",
       "      <td>514.367841</td>\n",
       "      <td>520.495522</td>\n",
       "      <td>436.748135</td>\n",
       "      <td>444.982467</td>\n",
       "      <td>444.579982</td>\n",
       "      <td>483.590251</td>\n",
       "      <td>460.975608</td>\n",
       "      <td>452.151844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30</td>\n",
       "      <td>554.735821</td>\n",
       "      <td>557.707577</td>\n",
       "      <td>457.091269</td>\n",
       "      <td>471.076755</td>\n",
       "      <td>467.106928</td>\n",
       "      <td>507.733480</td>\n",
       "      <td>492.710554</td>\n",
       "      <td>485.973747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>35</td>\n",
       "      <td>561.387477</td>\n",
       "      <td>563.086056</td>\n",
       "      <td>444.342425</td>\n",
       "      <td>467.850121</td>\n",
       "      <td>443.784262</td>\n",
       "      <td>514.195227</td>\n",
       "      <td>490.930837</td>\n",
       "      <td>475.622897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>40</td>\n",
       "      <td>589.026509</td>\n",
       "      <td>586.415565</td>\n",
       "      <td>458.135727</td>\n",
       "      <td>481.232031</td>\n",
       "      <td>468.696792</td>\n",
       "      <td>524.026545</td>\n",
       "      <td>508.955408</td>\n",
       "      <td>494.651855</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   No of VMs          RR         SJF         FWA        SQSA         BAT  \\\n",
       "0         10  326.134301  325.055427  305.999589  308.243038  313.967940   \n",
       "1         15  469.203217  470.027151  440.749030  442.265115  448.529936   \n",
       "2         20  521.887581  537.192201  471.518600  470.558280  477.285336   \n",
       "3         25  514.367841  520.495522  436.748135  444.982467  444.579982   \n",
       "4         30  554.735821  557.707577  457.091269  471.076755  467.106928   \n",
       "5         35  561.387477  563.086056  444.342425  467.850121  443.784262   \n",
       "6         40  589.026509  586.415565  458.135727  481.232031  468.696792   \n",
       "\n",
       "          PSO         BMO         SSA  \n",
       "0  316.306597  308.527742  309.106937  \n",
       "1  451.757597  444.996194  438.609238  \n",
       "2  492.576291  483.640668  469.892662  \n",
       "3  483.590251  460.975608  452.151844  \n",
       "4  507.733480  492.710554  485.973747  \n",
       "5  514.195227  490.930837  475.622897  \n",
       "6  524.026545  508.955408  494.651855  "
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cost_vm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "469a8d46-5bd2-4a41-a4b3-61fc807d341d",
   "metadata": {},
   "outputs": [],
   "source": [
    "costs=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "00f2d4ed-28d0-4a28-8055-e04d6944324e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of VMs:  10\n",
      "[(305.999589, 'FWA'), (308.243038, 'SQSA'), (308.527742, 'BMO'), (309.106937, 'SSA'), (313.96794, 'BAT'), (316.306597, 'PSO'), (325.055427, 'SJF'), (326.134301, 'RR')]\n",
      "Average Traditional:  325.59486400000003\n",
      "Average Metaheuristic:  310.35864050000004\n",
      "Slowest meta heuristic is lower in %:  2.85\n",
      "Average meta heuristic is lower in %:  4.68\n",
      "Fastest meta heuristic is lower in %:  6.02\n",
      "Best allocation algorithm:  (305.999589, 'FWA')\n",
      "Worst allocation algorithm:  (316.306597, 'PSO')\n",
      "\n",
      "No of VMs:  15\n",
      "[(438.609238, 'SSA'), (440.74903, 'FWA'), (442.265115, 'SQSA'), (444.996194, 'BMO'), (448.529936, 'BAT'), (451.757597, 'PSO'), (469.203217, 'RR'), (470.027151, 'SJF')]\n",
      "Average Traditional:  469.615184\n",
      "Average Metaheuristic:  444.48451833333326\n",
      "Slowest meta heuristic is lower in %:  3.8\n",
      "Average meta heuristic is lower in %:  5.35\n",
      "Fastest meta heuristic is lower in %:  6.6\n",
      "Best allocation algorithm:  (438.609238, 'SSA')\n",
      "Worst allocation algorithm:  (451.757597, 'PSO')\n",
      "\n",
      "No of VMs:  20\n",
      "[(469.892662, 'SSA'), (470.55828, 'SQSA'), (471.5186, 'FWA'), (477.285336, 'BAT'), (483.640668, 'BMO'), (492.576291, 'PSO'), (521.887581, 'RR'), (537.192201, 'SJF')]\n",
      "Average Traditional:  529.5398909999999\n",
      "Average Metaheuristic:  477.57863949999995\n",
      "Slowest meta heuristic is lower in %:  6.98\n",
      "Average meta heuristic is lower in %:  9.81\n",
      "Fastest meta heuristic is lower in %:  11.26\n",
      "Best allocation algorithm:  (469.892662, 'SSA')\n",
      "Worst allocation algorithm:  (492.576291, 'PSO')\n",
      "\n",
      "No of VMs:  25\n",
      "[(436.748135, 'FWA'), (444.579982, 'BAT'), (444.982467, 'SQSA'), (452.151844, 'SSA'), (460.975608, 'BMO'), (483.590251, 'PSO'), (514.367841, 'RR'), (520.495522, 'SJF')]\n",
      "Average Traditional:  517.4316815\n",
      "Average Metaheuristic:  453.83804783333335\n",
      "Slowest meta heuristic is lower in %:  6.54\n",
      "Average meta heuristic is lower in %:  12.29\n",
      "Fastest meta heuristic is lower in %:  15.59\n",
      "Best allocation algorithm:  (436.748135, 'FWA')\n",
      "Worst allocation algorithm:  (483.590251, 'PSO')\n",
      "\n",
      "No of VMs:  30\n",
      "[(457.091269, 'FWA'), (467.106928, 'BAT'), (471.076755, 'SQSA'), (485.973747, 'SSA'), (492.710554, 'BMO'), (507.73348, 'PSO'), (554.735821, 'RR'), (557.707577, 'SJF')]\n",
      "Average Traditional:  556.221699\n",
      "Average Metaheuristic:  480.2821221666666\n",
      "Slowest meta heuristic is lower in %:  8.72\n",
      "Average meta heuristic is lower in %:  13.65\n",
      "Fastest meta heuristic is lower in %:  17.82\n",
      "Best allocation algorithm:  (457.091269, 'FWA')\n",
      "Worst allocation algorithm:  (507.73348, 'PSO')\n",
      "\n",
      "No of VMs:  35\n",
      "[(443.784262, 'BAT'), (444.342425, 'FWA'), (467.850121, 'SQSA'), (475.622897, 'SSA'), (490.930837, 'BMO'), (514.195227, 'PSO'), (561.387477, 'RR'), (563.086056, 'SJF')]\n",
      "Average Traditional:  562.2367664999999\n",
      "Average Metaheuristic:  472.7876281666667\n",
      "Slowest meta heuristic is lower in %:  8.54\n",
      "Average meta heuristic is lower in %:  15.91\n",
      "Fastest meta heuristic is lower in %:  21.07\n",
      "Best allocation algorithm:  (443.784262, 'BAT')\n",
      "Worst allocation algorithm:  (514.195227, 'PSO')\n",
      "\n",
      "No of VMs:  40\n",
      "[(458.135727, 'FWA'), (468.696792, 'BAT'), (481.232031, 'SQSA'), (494.651855, 'SSA'), (508.955408, 'BMO'), (524.026545, 'PSO'), (586.415565, 'SJF'), (589.026509, 'RR')]\n",
      "Average Traditional:  587.721037\n",
      "Average Metaheuristic:  489.2830596666667\n",
      "Slowest meta heuristic is lower in %:  10.84\n",
      "Average meta heuristic is lower in %:  16.75\n",
      "Fastest meta heuristic is lower in %:  22.05\n",
      "Best allocation algorithm:  (458.135727, 'FWA')\n",
      "Worst allocation algorithm:  (524.026545, 'PSO')\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(df_cost_vm)):\n",
    "    row=df_cost_vm.iloc[i]\n",
    "    row_tuple=[(row[col],col) for col in df_cost_vm.columns]\n",
    "    \n",
    "    vm=row_tuple[0][0]\n",
    "    print(\"No of VMs: \",int(vm))\n",
    "    \n",
    "    row_tuple=row_tuple[1:]\n",
    "    row_tuple.sort()\n",
    "    print(row_tuple)\n",
    "    \n",
    "    average_traditional=(row_tuple[-1][0]+row_tuple[-2][0])/2\n",
    "    print(\"Average Traditional: \",average_traditional)\n",
    "    \n",
    "    average_meta=0\n",
    "    for j in range(6):\n",
    "        average_meta+=row_tuple[j][0]\n",
    "    average_meta/=6\n",
    "    \n",
    "    print(\"Average Metaheuristic: \",average_meta)\n",
    "    \n",
    "    slowest_percentage_meta_lower=(average_traditional-row_tuple[-3][0])/average_traditional*100\n",
    "    print(\"Slowest meta heuristic is lower in %: \", round(slowest_percentage_meta_lower,2))\n",
    "    \n",
    "    average_percentage_meta_lower=(average_traditional-average_meta)/average_traditional*100\n",
    "    print(\"Average meta heuristic is lower in %: \", round(average_percentage_meta_lower,2))\n",
    "    \n",
    "    fastest_percentage_meta_lower=(average_traditional-row_tuple[0][0])/average_traditional*100\n",
    "    print(\"Fastest meta heuristic is lower in %: \", round(fastest_percentage_meta_lower,2))\n",
    "    \n",
    "    print(\"Best allocation algorithm: \", row_tuple[0])\n",
    "    print(\"Worst allocation algorithm: \", row_tuple[-3])\n",
    "    costs.append((vm,average_traditional,average_meta,slowest_percentage_meta_lower,average_percentage_meta_lower,fastest_percentage_meta_lower,row_tuple[0][1],row_tuple[-3][1]))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "a015d3de-ea38-451e-8043-ac2bb8113674",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No of VMs</th>\n",
       "      <th>Average Traditional</th>\n",
       "      <th>Average Metaheuristic</th>\n",
       "      <th>Slowest meta heuristic is lower in %</th>\n",
       "      <th>Average meta heuristic is lower in %</th>\n",
       "      <th>Fastest meta heuristic is lower in %</th>\n",
       "      <th>Best allocation</th>\n",
       "      <th>Worst allocation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.0</td>\n",
       "      <td>325.594864</td>\n",
       "      <td>310.358641</td>\n",
       "      <td>2.852707</td>\n",
       "      <td>4.679504</td>\n",
       "      <td>6.018300</td>\n",
       "      <td>FWA</td>\n",
       "      <td>PSO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.0</td>\n",
       "      <td>469.615184</td>\n",
       "      <td>444.484518</td>\n",
       "      <td>3.802600</td>\n",
       "      <td>5.351332</td>\n",
       "      <td>6.602416</td>\n",
       "      <td>SSA</td>\n",
       "      <td>PSO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20.0</td>\n",
       "      <td>529.539891</td>\n",
       "      <td>477.578639</td>\n",
       "      <td>6.980324</td>\n",
       "      <td>9.812528</td>\n",
       "      <td>11.263973</td>\n",
       "      <td>SSA</td>\n",
       "      <td>PSO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25.0</td>\n",
       "      <td>517.431681</td>\n",
       "      <td>453.838048</td>\n",
       "      <td>6.540270</td>\n",
       "      <td>12.290247</td>\n",
       "      <td>15.593082</td>\n",
       "      <td>FWA</td>\n",
       "      <td>PSO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30.0</td>\n",
       "      <td>556.221699</td>\n",
       "      <td>480.282122</td>\n",
       "      <td>8.717427</td>\n",
       "      <td>13.652753</td>\n",
       "      <td>17.822108</td>\n",
       "      <td>FWA</td>\n",
       "      <td>PSO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>35.0</td>\n",
       "      <td>562.236766</td>\n",
       "      <td>472.787628</td>\n",
       "      <td>8.544717</td>\n",
       "      <td>15.909514</td>\n",
       "      <td>21.068082</td>\n",
       "      <td>BAT</td>\n",
       "      <td>PSO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>40.0</td>\n",
       "      <td>587.721037</td>\n",
       "      <td>489.283060</td>\n",
       "      <td>10.837538</td>\n",
       "      <td>16.749099</td>\n",
       "      <td>22.048779</td>\n",
       "      <td>FWA</td>\n",
       "      <td>PSO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   No of VMs  Average Traditional  Average Metaheuristic  \\\n",
       "0       10.0           325.594864             310.358641   \n",
       "1       15.0           469.615184             444.484518   \n",
       "2       20.0           529.539891             477.578639   \n",
       "3       25.0           517.431681             453.838048   \n",
       "4       30.0           556.221699             480.282122   \n",
       "5       35.0           562.236766             472.787628   \n",
       "6       40.0           587.721037             489.283060   \n",
       "\n",
       "   Slowest meta heuristic is lower in %  Average meta heuristic is lower in %  \\\n",
       "0                              2.852707                              4.679504   \n",
       "1                              3.802600                              5.351332   \n",
       "2                              6.980324                              9.812528   \n",
       "3                              6.540270                             12.290247   \n",
       "4                              8.717427                             13.652753   \n",
       "5                              8.544717                             15.909514   \n",
       "6                             10.837538                             16.749099   \n",
       "\n",
       "   Fastest meta heuristic is lower in % Best allocation Worst allocation  \n",
       "0                              6.018300             FWA              PSO  \n",
       "1                              6.602416             SSA              PSO  \n",
       "2                             11.263973             SSA              PSO  \n",
       "3                             15.593082             FWA              PSO  \n",
       "4                             17.822108             FWA              PSO  \n",
       "5                             21.068082             BAT              PSO  \n",
       "6                             22.048779             FWA              PSO  "
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "costs_df=pd.DataFrame(costs,columns=[\"No of VMs\",\"Average Traditional\",\"Average Metaheuristic\",\"Slowest meta heuristic is lower in %\",\n",
    "                            \"Average meta heuristic is lower in %\", \"Fastest meta heuristic is lower in %\", \n",
    "                            \"Best allocation\",\"Worst allocation\"])\n",
    "costs_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "79fec792-db8a-4d3d-8771-5a4f19df7e22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "bar_width = 0.25\n",
    "index = np.arange(len(costs_df))\n",
    "\n",
    "bars3 = ax.barh(index, costs_df[\"Slowest meta heuristic is lower in %\"], color='lightgreen', height=bar_width, label=\"Worst Performing Metaheuristic\")\n",
    "bars1 = ax.barh(index + bar_width, costs_df[\"Average meta heuristic is lower in %\"], color='salmon', height=bar_width, label=\"Average Metaheuristic\")\n",
    "bars2 = ax.barh(index + 2*bar_width, costs_df[\"Fastest meta heuristic is lower in %\"], color='turquoise', height=bar_width, label=\"Best Performing Metaheuristic\")\n",
    "\n",
    "ax.set_yticks(index + bar_width)\n",
    "ax.set_yticklabels(costs_df[\"No of VMs\"]) \n",
    "ax.set_xlabel(\"Percentage Lower\")\n",
    "ax.set_ylabel(\"Number of VMs\")\n",
    "ax.set_title(\"Decrease in cost from the average of traditional allocation\")\n",
    "\n",
    "ax.legend()\n",
    "ax.invert_yaxis()\n",
    "\n",
    "for bar in bars1:\n",
    "    width = bar.get_width()\n",
    "    ax.text(width - 1, bar.get_y() + bar.get_height() / 2, f'{width:.2f}%', \n",
    "            ha='center', va='center', color='black', fontsize=10)\n",
    "\n",
    "for bar, algo in zip(bars2, costs_df[\"Best allocation\"]):\n",
    "    width = bar.get_width()\n",
    "    ax.text(width - 1, bar.get_y() + bar.get_height() / 2, f'{width:.2f}%', \n",
    "            ha='center', va='center', color='black', fontsize=10)\n",
    "    ax.text(width + 0.1, bar.get_y() + bar.get_height() / 2, algo, \n",
    "            ha='left', va='center', color='black', fontsize=10)\n",
    "\n",
    "for bar, algo in zip(bars3, costs_df[\"Worst allocation\"]):\n",
    "    width = bar.get_width()\n",
    "    ax.text(width - 1, bar.get_y() + bar.get_height() / 2, f'{width:.2f}%', \n",
    "            ha='center', va='center', color='black', fontsize=10)\n",
    "    ax.text(width + 0.1, bar.get_y() + bar.get_height() / 2, algo, \n",
    "            ha='left', va='center', color='black', fontsize=10)\n",
    "\n",
    "plt.savefig(\"cost_decrease_perc.png\", bbox_inches='tight') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19123d8f-93ab-45d1-ba8e-8a8d22880527",
   "metadata": {},
   "source": [
    "## Among metaheuristics->"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "264e86ac-00f9-41a2-a4d9-864f49f47462",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No of VMs</th>\n",
       "      <th>FWA</th>\n",
       "      <th>SQSA</th>\n",
       "      <th>BAT</th>\n",
       "      <th>PSO</th>\n",
       "      <th>BMO</th>\n",
       "      <th>SSA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>305.999589</td>\n",
       "      <td>308.243038</td>\n",
       "      <td>313.967940</td>\n",
       "      <td>316.306597</td>\n",
       "      <td>308.527742</td>\n",
       "      <td>309.106937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15</td>\n",
       "      <td>440.749030</td>\n",
       "      <td>442.265115</td>\n",
       "      <td>448.529936</td>\n",
       "      <td>451.757597</td>\n",
       "      <td>444.996194</td>\n",
       "      <td>438.609238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>471.518600</td>\n",
       "      <td>470.558280</td>\n",
       "      <td>477.285336</td>\n",
       "      <td>492.576291</td>\n",
       "      <td>483.640668</td>\n",
       "      <td>469.892662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25</td>\n",
       "      <td>436.748135</td>\n",
       "      <td>444.982467</td>\n",
       "      <td>444.579982</td>\n",
       "      <td>483.590251</td>\n",
       "      <td>460.975608</td>\n",
       "      <td>452.151844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30</td>\n",
       "      <td>457.091269</td>\n",
       "      <td>471.076755</td>\n",
       "      <td>467.106928</td>\n",
       "      <td>507.733480</td>\n",
       "      <td>492.710554</td>\n",
       "      <td>485.973747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>35</td>\n",
       "      <td>444.342425</td>\n",
       "      <td>467.850121</td>\n",
       "      <td>443.784262</td>\n",
       "      <td>514.195227</td>\n",
       "      <td>490.930837</td>\n",
       "      <td>475.622897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>40</td>\n",
       "      <td>458.135727</td>\n",
       "      <td>481.232031</td>\n",
       "      <td>468.696792</td>\n",
       "      <td>524.026545</td>\n",
       "      <td>508.955408</td>\n",
       "      <td>494.651855</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   No of VMs         FWA        SQSA         BAT         PSO         BMO  \\\n",
       "0         10  305.999589  308.243038  313.967940  316.306597  308.527742   \n",
       "1         15  440.749030  442.265115  448.529936  451.757597  444.996194   \n",
       "2         20  471.518600  470.558280  477.285336  492.576291  483.640668   \n",
       "3         25  436.748135  444.982467  444.579982  483.590251  460.975608   \n",
       "4         30  457.091269  471.076755  467.106928  507.733480  492.710554   \n",
       "5         35  444.342425  467.850121  443.784262  514.195227  490.930837   \n",
       "6         40  458.135727  481.232031  468.696792  524.026545  508.955408   \n",
       "\n",
       "          SSA  \n",
       "0  309.106937  \n",
       "1  438.609238  \n",
       "2  469.892662  \n",
       "3  452.151844  \n",
       "4  485.973747  \n",
       "5  475.622897  \n",
       "6  494.651855  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cost_vm_meta=df_cost_vm.drop([\"SJF\",\"RR\"],axis=1)\n",
    "df_cost_vm_meta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "876f1d59-7008-4561-b161-8030af42edcb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of VMs:  10\n",
      "[(305.999589, 'FWA'), (308.243038, 'SQSA'), (308.527742, 'BMO'), (309.106937, 'SSA'), (313.96794, 'BAT'), (316.306597, 'PSO')]\n",
      "Best allocation algorithm:  (305.999589, 'FWA')\n",
      "Worst allocation algorithm:  (316.306597, 'PSO')\n",
      "Best metaheuristic is lower than worst in %:  3.2585498050804156\n",
      "\n",
      "No of VMs:  15\n",
      "[(438.609238, 'SSA'), (440.74903, 'FWA'), (442.265115, 'SQSA'), (444.996194, 'BMO'), (448.529936, 'BAT'), (451.757597, 'PSO')]\n",
      "Best allocation algorithm:  (438.609238, 'SSA')\n",
      "Worst allocation algorithm:  (451.757597, 'PSO')\n",
      "Best metaheuristic is lower than worst in %:  2.910489848386539\n",
      "\n",
      "No of VMs:  20\n",
      "[(469.892662, 'SSA'), (470.55828, 'SQSA'), (471.5186, 'FWA'), (477.285336, 'BAT'), (483.640668, 'BMO'), (492.576291, 'PSO')]\n",
      "Best allocation algorithm:  (469.892662, 'SSA')\n",
      "Worst allocation algorithm:  (492.576291, 'PSO')\n",
      "Best metaheuristic is lower than worst in %:  4.60509963927599\n",
      "\n",
      "No of VMs:  25\n",
      "[(436.748135, 'FWA'), (444.579982, 'BAT'), (444.982467, 'SQSA'), (452.151844, 'SSA'), (460.975608, 'BMO'), (483.590251, 'PSO')]\n",
      "Best allocation algorithm:  (436.748135, 'FWA')\n",
      "Worst allocation algorithm:  (483.590251, 'PSO')\n",
      "Best metaheuristic is lower than worst in %:  9.686323473878309\n",
      "\n",
      "No of VMs:  30\n",
      "[(457.091269, 'FWA'), (467.106928, 'BAT'), (471.076755, 'SQSA'), (485.973747, 'SSA'), (492.710554, 'BMO'), (507.73348, 'PSO')]\n",
      "Best allocation algorithm:  (457.091269, 'FWA')\n",
      "Worst allocation algorithm:  (507.73348, 'PSO')\n",
      "Best metaheuristic is lower than worst in %:  9.974172079414572\n",
      "\n",
      "No of VMs:  35\n",
      "[(443.784262, 'BAT'), (444.342425, 'FWA'), (467.850121, 'SQSA'), (475.622897, 'SSA'), (490.930837, 'BMO'), (514.195227, 'PSO')]\n",
      "Best allocation algorithm:  (443.784262, 'BAT')\n",
      "Worst allocation algorithm:  (514.195227, 'PSO')\n",
      "Best metaheuristic is lower than worst in %:  13.693430297049417\n",
      "\n",
      "No of VMs:  40\n",
      "[(458.135727, 'FWA'), (468.696792, 'BAT'), (481.232031, 'SQSA'), (494.651855, 'SSA'), (508.955408, 'BMO'), (524.026545, 'PSO')]\n",
      "Best allocation algorithm:  (458.135727, 'FWA')\n",
      "Worst allocation algorithm:  (524.026545, 'PSO')\n",
      "Best metaheuristic is lower than worst in %:  12.573946611807626\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(df_cost_vm_meta)):\n",
    "    row=df_cost_vm_meta.iloc[i]\n",
    "    row_tuple=[(row[col],col) for col in df_cost_vm_meta.columns]\n",
    "    vm=row_tuple[0][0]\n",
    "    print(\"No of VMs: \",int(vm))\n",
    "    row_tuple=row_tuple[1:]\n",
    "    row_tuple.sort()\n",
    "    print(row_tuple)\n",
    "    print(\"Best allocation algorithm: \", row_tuple[0])\n",
    "    print(\"Worst allocation algorithm: \", row_tuple[-1])\n",
    "    fastest_slowest_perc=(row_tuple[-1][0]-row_tuple[0][0])/row_tuple[-1][0]*100\n",
    "    print(\"Best metaheuristic is lower than worst in %: \", fastest_slowest_perc)\n",
    "    # costs.append((vm,average_traditional,average_meta,average_percentage_meta_lower,fastest_percentage_meta_lower,row_tuple[0][1]))\n",
    "    \n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6044d055-aecc-4cfe-b86d-6f3eddcf4724",
   "metadata": {},
   "source": [
    "# ENERGY COMPARISON (VARYING VM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "50a47768-11d5-434c-96d5-d04f924afde1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_energy_vm=pd.read_excel(\"energy vs vm 2.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "16a3664b-c7cd-48b7-aa3b-bfdc7f931e76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No of VMs</th>\n",
       "      <th>RR</th>\n",
       "      <th>SJF</th>\n",
       "      <th>FWA</th>\n",
       "      <th>SQSA</th>\n",
       "      <th>BAT</th>\n",
       "      <th>PSO</th>\n",
       "      <th>BMO</th>\n",
       "      <th>SSA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>1.690669</td>\n",
       "      <td>1.681327</td>\n",
       "      <td>1.539971</td>\n",
       "      <td>1.542770</td>\n",
       "      <td>1.592983</td>\n",
       "      <td>1.602920</td>\n",
       "      <td>1.541886</td>\n",
       "      <td>1.538309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15</td>\n",
       "      <td>2.713577</td>\n",
       "      <td>2.737240</td>\n",
       "      <td>2.492727</td>\n",
       "      <td>2.437267</td>\n",
       "      <td>2.564152</td>\n",
       "      <td>2.542549</td>\n",
       "      <td>2.480072</td>\n",
       "      <td>2.441914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>2.865236</td>\n",
       "      <td>3.015303</td>\n",
       "      <td>2.482737</td>\n",
       "      <td>2.293156</td>\n",
       "      <td>2.492973</td>\n",
       "      <td>2.574584</td>\n",
       "      <td>2.464807</td>\n",
       "      <td>2.350688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25</td>\n",
       "      <td>2.881187</td>\n",
       "      <td>2.973211</td>\n",
       "      <td>2.181896</td>\n",
       "      <td>2.154178</td>\n",
       "      <td>2.304066</td>\n",
       "      <td>2.587685</td>\n",
       "      <td>2.324047</td>\n",
       "      <td>2.203588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30</td>\n",
       "      <td>3.431461</td>\n",
       "      <td>3.463336</td>\n",
       "      <td>2.452345</td>\n",
       "      <td>2.404746</td>\n",
       "      <td>2.589216</td>\n",
       "      <td>2.868654</td>\n",
       "      <td>2.707426</td>\n",
       "      <td>2.638588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>35</td>\n",
       "      <td>3.450209</td>\n",
       "      <td>3.462311</td>\n",
       "      <td>2.306849</td>\n",
       "      <td>2.357581</td>\n",
       "      <td>2.314211</td>\n",
       "      <td>2.928384</td>\n",
       "      <td>2.607616</td>\n",
       "      <td>2.466589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>40</td>\n",
       "      <td>3.725988</td>\n",
       "      <td>3.693244</td>\n",
       "      <td>2.386503</td>\n",
       "      <td>2.437955</td>\n",
       "      <td>2.534019</td>\n",
       "      <td>2.960306</td>\n",
       "      <td>2.788281</td>\n",
       "      <td>2.604782</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   No of VMs        RR       SJF       FWA      SQSA       BAT       PSO  \\\n",
       "0         10  1.690669  1.681327  1.539971  1.542770  1.592983  1.602920   \n",
       "1         15  2.713577  2.737240  2.492727  2.437267  2.564152  2.542549   \n",
       "2         20  2.865236  3.015303  2.482737  2.293156  2.492973  2.574584   \n",
       "3         25  2.881187  2.973211  2.181896  2.154178  2.304066  2.587685   \n",
       "4         30  3.431461  3.463336  2.452345  2.404746  2.589216  2.868654   \n",
       "5         35  3.450209  3.462311  2.306849  2.357581  2.314211  2.928384   \n",
       "6         40  3.725988  3.693244  2.386503  2.437955  2.534019  2.960306   \n",
       "\n",
       "        BMO       SSA  \n",
       "0  1.541886  1.538309  \n",
       "1  2.480072  2.441914  \n",
       "2  2.464807  2.350688  \n",
       "3  2.324047  2.203588  \n",
       "4  2.707426  2.638588  \n",
       "5  2.607616  2.466589  \n",
       "6  2.788281  2.604782  "
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_energy_vm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "00045fba-c2ed-471c-959e-ea0e74d2ca05",
   "metadata": {},
   "outputs": [],
   "source": [
    "energies=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "c361b7ca-b243-4c2c-9fdd-b3fa5b339934",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of VMs:  10\n",
      "[(1.538309, 'SSA'), (1.539971, 'FWA'), (1.541886, 'BMO'), (1.54277, 'SQSA'), (1.592983, 'BAT'), (1.60292, 'PSO'), (1.681327, 'SJF'), (1.690669, 'RR')]\n",
      "Average Traditional:  1.685998\n",
      "Average Metaheuristic:  1.5598064999999999\n",
      "Slowest meta heuristic is lower in %:  4.93\n",
      "Average meta heuristic is lower in %:  7.48\n",
      "Fastest meta heuristic is lower in %:  8.76\n",
      "Best allocation algorithm:  (1.538309, 'SSA')\n",
      "Worst allocation algorithm:  (1.60292, 'PSO')\n",
      "\n",
      "No of VMs:  15\n",
      "[(2.437267, 'SQSA'), (2.441914, 'SSA'), (2.480072, 'BMO'), (2.492727, 'FWA'), (2.542549, 'PSO'), (2.564152, 'BAT'), (2.713577, 'RR'), (2.73724, 'SJF')]\n",
      "Average Traditional:  2.7254085\n",
      "Average Metaheuristic:  2.4931134999999998\n",
      "Slowest meta heuristic is lower in %:  5.92\n",
      "Average meta heuristic is lower in %:  8.52\n",
      "Fastest meta heuristic is lower in %:  10.57\n",
      "Best allocation algorithm:  (2.437267, 'SQSA')\n",
      "Worst allocation algorithm:  (2.564152, 'BAT')\n",
      "\n",
      "No of VMs:  20\n",
      "[(2.293156, 'SQSA'), (2.350688, 'SSA'), (2.464807, 'BMO'), (2.482737, 'FWA'), (2.492973, 'BAT'), (2.574584, 'PSO'), (2.865236, 'RR'), (3.015303, 'SJF')]\n",
      "Average Traditional:  2.9402695\n",
      "Average Metaheuristic:  2.4431575000000003\n",
      "Slowest meta heuristic is lower in %:  12.44\n",
      "Average meta heuristic is lower in %:  16.91\n",
      "Fastest meta heuristic is lower in %:  22.01\n",
      "Best allocation algorithm:  (2.293156, 'SQSA')\n",
      "Worst allocation algorithm:  (2.574584, 'PSO')\n",
      "\n",
      "No of VMs:  25\n",
      "[(2.154178, 'SQSA'), (2.181896, 'FWA'), (2.203588, 'SSA'), (2.304066, 'BAT'), (2.324047, 'BMO'), (2.587685, 'PSO'), (2.881187, 'RR'), (2.973211, 'SJF')]\n",
      "Average Traditional:  2.927199\n",
      "Average Metaheuristic:  2.2925766666666667\n",
      "Slowest meta heuristic is lower in %:  11.6\n",
      "Average meta heuristic is lower in %:  21.68\n",
      "Fastest meta heuristic is lower in %:  26.41\n",
      "Best allocation algorithm:  (2.154178, 'SQSA')\n",
      "Worst allocation algorithm:  (2.587685, 'PSO')\n",
      "\n",
      "No of VMs:  30\n",
      "[(2.404746, 'SQSA'), (2.452345, 'FWA'), (2.589216, 'BAT'), (2.638588, 'SSA'), (2.707426, 'BMO'), (2.868654, 'PSO'), (3.431461, 'RR'), (3.463336, 'SJF')]\n",
      "Average Traditional:  3.4473985000000003\n",
      "Average Metaheuristic:  2.6101625\n",
      "Slowest meta heuristic is lower in %:  16.79\n",
      "Average meta heuristic is lower in %:  24.29\n",
      "Fastest meta heuristic is lower in %:  30.24\n",
      "Best allocation algorithm:  (2.404746, 'SQSA')\n",
      "Worst allocation algorithm:  (2.868654, 'PSO')\n",
      "\n",
      "No of VMs:  35\n",
      "[(2.306849, 'FWA'), (2.314211, 'BAT'), (2.357581, 'SQSA'), (2.466589, 'SSA'), (2.607616, 'BMO'), (2.928384, 'PSO'), (3.450209, 'RR'), (3.462311, 'SJF')]\n",
      "Average Traditional:  3.4562600000000003\n",
      "Average Metaheuristic:  2.4968716666666664\n",
      "Slowest meta heuristic is lower in %:  15.27\n",
      "Average meta heuristic is lower in %:  27.76\n",
      "Fastest meta heuristic is lower in %:  33.26\n",
      "Best allocation algorithm:  (2.306849, 'FWA')\n",
      "Worst allocation algorithm:  (2.928384, 'PSO')\n",
      "\n",
      "No of VMs:  40\n",
      "[(2.386503, 'FWA'), (2.437955, 'SQSA'), (2.534019, 'BAT'), (2.604782, 'SSA'), (2.788281, 'BMO'), (2.960306, 'PSO'), (3.693244, 'SJF'), (3.725988, 'RR')]\n",
      "Average Traditional:  3.709616\n",
      "Average Metaheuristic:  2.618641\n",
      "Slowest meta heuristic is lower in %:  20.2\n",
      "Average meta heuristic is lower in %:  29.41\n",
      "Fastest meta heuristic is lower in %:  35.67\n",
      "Best allocation algorithm:  (2.386503, 'FWA')\n",
      "Worst allocation algorithm:  (2.960306, 'PSO')\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(df_energy_vm)):\n",
    "    row=df_energy_vm.iloc[i]\n",
    "    row_tuple=[(row[col],col) for col in df_energy_vm.columns]\n",
    "    \n",
    "    vm=row_tuple[0][0]\n",
    "    print(\"No of VMs: \",int(vm))\n",
    "    \n",
    "    row_tuple=row_tuple[1:]\n",
    "    row_tuple.sort()\n",
    "    print(row_tuple)\n",
    "    \n",
    "    average_traditional=(row_tuple[-1][0]+row_tuple[-2][0])/2\n",
    "    print(\"Average Traditional: \",average_traditional)\n",
    "    \n",
    "    average_meta=0\n",
    "    for j in range(6):\n",
    "        average_meta+=row_tuple[j][0]\n",
    "    average_meta/=6\n",
    "    \n",
    "    print(\"Average Metaheuristic: \",average_meta)\n",
    "    \n",
    "    slowest_percentage_meta_lower=(average_traditional-row_tuple[-3][0])/average_traditional*100\n",
    "    print(\"Slowest meta heuristic is lower in %: \", round(slowest_percentage_meta_lower,2))\n",
    "    \n",
    "    average_percentage_meta_lower=(average_traditional-average_meta)/average_traditional*100\n",
    "    print(\"Average meta heuristic is lower in %: \", round(average_percentage_meta_lower,2))\n",
    "    \n",
    "    fastest_percentage_meta_lower=(average_traditional-row_tuple[0][0])/average_traditional*100\n",
    "    print(\"Fastest meta heuristic is lower in %: \", round(fastest_percentage_meta_lower,2))\n",
    "    \n",
    "    print(\"Best allocation algorithm: \", row_tuple[0])\n",
    "    print(\"Worst allocation algorithm: \", row_tuple[-3])\n",
    "    energies.append((vm,average_traditional,average_meta,slowest_percentage_meta_lower,average_percentage_meta_lower,fastest_percentage_meta_lower,row_tuple[0][1],row_tuple[-3][1]))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "80241134-248c-4b85-aa5b-94662b7eccf0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No of VMs</th>\n",
       "      <th>Average Traditional</th>\n",
       "      <th>Average Metaheuristic</th>\n",
       "      <th>Slowest meta heuristic is lower in %</th>\n",
       "      <th>Average meta heuristic is lower in %</th>\n",
       "      <th>Fastest meta heuristic is lower in %</th>\n",
       "      <th>Best allocation</th>\n",
       "      <th>Worst allocation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.0</td>\n",
       "      <td>1.685998</td>\n",
       "      <td>1.559806</td>\n",
       "      <td>4.927527</td>\n",
       "      <td>7.484677</td>\n",
       "      <td>8.759738</td>\n",
       "      <td>SSA</td>\n",
       "      <td>PSO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.0</td>\n",
       "      <td>2.725408</td>\n",
       "      <td>2.493113</td>\n",
       "      <td>5.916783</td>\n",
       "      <td>8.523309</td>\n",
       "      <td>10.572415</td>\n",
       "      <td>SQSA</td>\n",
       "      <td>BAT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20.0</td>\n",
       "      <td>2.940269</td>\n",
       "      <td>2.443158</td>\n",
       "      <td>12.437142</td>\n",
       "      <td>16.907022</td>\n",
       "      <td>22.008646</td>\n",
       "      <td>SQSA</td>\n",
       "      <td>PSO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25.0</td>\n",
       "      <td>2.927199</td>\n",
       "      <td>2.292577</td>\n",
       "      <td>11.598596</td>\n",
       "      <td>21.680191</td>\n",
       "      <td>26.408215</td>\n",
       "      <td>SQSA</td>\n",
       "      <td>PSO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30.0</td>\n",
       "      <td>3.447399</td>\n",
       "      <td>2.610162</td>\n",
       "      <td>16.787862</td>\n",
       "      <td>24.286023</td>\n",
       "      <td>30.244618</td>\n",
       "      <td>SQSA</td>\n",
       "      <td>PSO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>35.0</td>\n",
       "      <td>3.456260</td>\n",
       "      <td>2.496872</td>\n",
       "      <td>15.273041</td>\n",
       "      <td>27.757991</td>\n",
       "      <td>33.255918</td>\n",
       "      <td>FWA</td>\n",
       "      <td>PSO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>40.0</td>\n",
       "      <td>3.709616</td>\n",
       "      <td>2.618641</td>\n",
       "      <td>20.199126</td>\n",
       "      <td>29.409378</td>\n",
       "      <td>35.667115</td>\n",
       "      <td>FWA</td>\n",
       "      <td>PSO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   No of VMs  Average Traditional  Average Metaheuristic  \\\n",
       "0       10.0             1.685998               1.559806   \n",
       "1       15.0             2.725408               2.493113   \n",
       "2       20.0             2.940269               2.443158   \n",
       "3       25.0             2.927199               2.292577   \n",
       "4       30.0             3.447399               2.610162   \n",
       "5       35.0             3.456260               2.496872   \n",
       "6       40.0             3.709616               2.618641   \n",
       "\n",
       "   Slowest meta heuristic is lower in %  Average meta heuristic is lower in %  \\\n",
       "0                              4.927527                              7.484677   \n",
       "1                              5.916783                              8.523309   \n",
       "2                             12.437142                             16.907022   \n",
       "3                             11.598596                             21.680191   \n",
       "4                             16.787862                             24.286023   \n",
       "5                             15.273041                             27.757991   \n",
       "6                             20.199126                             29.409378   \n",
       "\n",
       "   Fastest meta heuristic is lower in % Best allocation Worst allocation  \n",
       "0                              8.759738             SSA              PSO  \n",
       "1                             10.572415            SQSA              BAT  \n",
       "2                             22.008646            SQSA              PSO  \n",
       "3                             26.408215            SQSA              PSO  \n",
       "4                             30.244618            SQSA              PSO  \n",
       "5                             33.255918             FWA              PSO  \n",
       "6                             35.667115             FWA              PSO  "
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "energies_df=pd.DataFrame(energies,columns=[\"No of VMs\",\"Average Traditional\",\"Average Metaheuristic\",\"Slowest meta heuristic is lower in %\",\n",
    "                            \"Average meta heuristic is lower in %\", \"Fastest meta heuristic is lower in %\", \n",
    "                            \"Best allocation\",\"Worst allocation\"])\n",
    "energies_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "558c044b-a448-4c89-97f6-9a8eb34b9eaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nzvX+3N+XEydOkDNnTooWLaqzvXbt2qxdu5bz589Trly5BMclx/t1rlu3LnXr1iUyMpI7d+4QHBzMpUuXiI2NVet86tQpDA0NKVu2rHqcmZkZ5cqV4+TJk59Vji+5n99/z3B0dGT58uVotVru3r3L7du3uXHjBv/8888nh6u+Kynt/zHnzp0jKiqKWrVq6WwvVqwYOXPm5Pjx4zRt2pTTp09Trlw5jI2N1TSurq7s3btXfV26dGmio6O5desWt2/f5tq1azx//lz9vf2UlLy+QnyPJNAS4gf1+PFjTExMyJgxIy9fvgRQ5/i8LyQkhFevXqEoCpkzZ/5k3u+mic97xIgRjBgxItG8IW7ey7Bhw9i9ezcajYbcuXOrH+SU/38Gy08//YRWq2X58uVMnz6dqVOnkjNnTnr37k2NGjXUc9WoUeODdU4OExMTndd6enoffR7My5cv2bp1K1u3bk2w7/3J3x/LOzn1iA9O4r148SLRNno/3U8//cSoUaPYtGkT7dq1Y9u2bbRs2fIDNfu4xNr7/fPFb3t3DhLEBYHve//afK73V7lL7Bp/7J7/kE2bNjFp0iQePnxIxowZKViwYIIyV6tWjZEjR6rX9a+//qJKlSpqmV6+fElwcDCFChVK9BzvLoX+/rVMyu/K8+fPE807a9as6hyxz/19efXq1QfbFz4vKP5QXvEiIiIYOXIkGzduJCYmBhsbG9zd3TEwMFDr/OrVKzJmzIienu708qxZs352OZJ7P3/qPWPhwoXMmTOHFy9ekCVLFgoVKoSpqWmC+ZIfk5T2/5j4eVgfqlN8WV6+fPnR93utVsukSZNYtmwZYWFhZM+eHVdXV53A7FNS+voK8b2RQEuIH1BsbCwnTpygSJEi6OvrkyFDBgAmTJiAnZ1dgvRZsmQhXbp0aDSaBBP8o6KiOHr0KK6uromeKz7vfv366SzxG8/CwgKAPn36EBQUxMKFCylSpAhGRkaEh4ezZs0anfQ1a9akZs2avHnzhsOHDzNv3jz69u1LsWLF1HP9+eefmJubJzhXjhw5PnFlvkz69OkpVaoUrVu3TrDPwCDpb6dfUg8rKyuCg4MTbH/27JnOa3Nzc6pVq8a2bdtwdHTk9evX1K1bN8ll/JD4b7KfPn2aYN+TJ0/IlCnTF58jJSTlnk/MqVOn6N+/Pz4+PrRt2xZra2sAxo0bp/aWQVwAWbFiRbZt20bp0qW5evUq/fv3V/enT5+eEiVK0K9fv0TPY2Rk9MGyJ+V3xdraOkGbg+598Ln3mYWFRaL3WHwA9zXa2NfXlx07djBlyhRKlSql9uJ6enqqaTJlysSLFy+IjY1FX19f3R7/Yf5zpOT9vHnzZsaMGUPv3r1p2LCh+uVLjx491B6ppEjqe+WHxL/nPn36lHz58unse/LkCba2tkDcPfr++z3AgQMHKFiwIOvXr2fRokUMHz6cqlWrkj59egAaNmyY5Lp8L+8XQnwtsuqgED+glStXEhISoj4o1M3NDUNDQx4/foyLi4v6z9DQkIkTJ3Lv3j3Mzc1xdHRkz549OnkdPnyYDh068OjRo0TPlTdvXjJnzsy9e/d08ra2tmbixInqN5anT5+matWqlCxZUv2QefDgQQB11bCePXvStWtXIO5DQPXq1encuTOxsbGEhIRQvHhxIK5X591zvXz5kilTpnzRB66kKFGiBDdv3sTR0VE9t7OzM4sWLWLXrl1JzudL6lGiRAnu3r3LlStX1G2RkZHqtXxXw4YNuX79OgsWLKBkyZIpEojmyZOHrFmzsnnzZp3td+/e5dy5cxQpUuSLz/G+93swkiIp93xizp49i1arpXv37mqQFRsbqw4NfXeFuzp16nD+/HmWLVtGtmzZKFmypLqvRIkS3Lp1izx58uicf9OmTaxZs0YnUHhfUn5XihcvztmzZ9XgB+I+uJ47d059/bn3WfHixbl//75OYAlxPX2GhoYf/NIlMUltu9OnT+Ph4UGlSpXUIOvixYs8f/5crbOnpycxMTHs3r1bPS4qKoojR458dhlS8n4+ffo06dOnp0OHDmqQFRoayunTpxOsjPipfD7V/h/j5uaGkZFRgjqdOnWKBw8eqHUqVqwYhw4dIioqSk1z7do1OnToQGBgIKdPnyZ//vw0bNhQDbIeP37M9evXdcrxra6vEN8j6dES4jv29u1b9YOVVqvlxYsXHD58mFWrVlG7dm2qVKkCxH0T3K5dO6ZOncrbt2/x8PDg8ePHTJ06FY1GQ8GCBQHo3r07v/76Kz179qR+/fo8f/6ciRMnUqFCBRwdHXU+3MfT19fnt99+Y+jQoejr61OhQgVev37NzJkzefz4sTq8ydXVlc2bN1OoUCGsra05e/Ysc+bMQaPRqMOoSpYsybBhwxg7dixly5bl9evXTJ8+HTs7OwoWLIihoSG1a9dmyJAh3L9/H2dnZ27dusXkyZOxsbFJtOciJXXu3JnGjRvTsWNHmjRpgrGxMatWrWL37t1MmzYtyfnY29t/dj1q1qzJ3Llz6dKlCz169CBDhgwsWLCAZ8+eJQikihYtSt68eTlx4gQTJkz43Grr0NPTo1evXgwcOJDffvuNunXr8uLFC6ZPn46FhUWivX1fKkOGDDx9+pQDBw7g6OiYpGOSes+/Lz6I+N///keDBg14/fo1S5cu5erVq0DcvJn44ZClS5fG0tKSlStX0qpVK50PnK1atWLjxo20atWKNm3akClTJrZu3crq1asZOHDgR8uelN+VFi1asGzZMtq2bUuXLl0AmDFjBlFRUeqcxs+9z+rXr8/y5cvp2rUr3bt3x9bWlr179xIQEEDXrl3VnrKkiE/7119/4ebmpvamJFbnbdu2sWLFCvLly8fVq1eZNWuWTp09PT0pXbo0gwcP5tmzZ+TMmZPFixfz/Pnzjw6By5AhA5cvX+bEiRMJgsSUvJ9dXV1ZsWIFY8aMoUKFCoSEhODv78/Tp0/VXqak5vOp9v+YjBkz0qFDB6ZPn46hoSEVK1bk3r17TJ06lfz581O/fn0g7v3sl19+oX379rRs2ZKoqCimTp1KoUKFKFu2LFeuXGHmzJnMnTuXwoULExwczJw5c4iKitIpx7e6vkJ8jyTQEuI7dvnyZX755Rcg7g9a5syZyZMnD2PGjEkwEbpnz55kzZqV5cuXM3/+fCwsLPD09KRXr17qt5UVKlRgzpw5+Pn50aVLFzJlykT16tXp0aPHR8vRqFEjzM3NmT9/PqtWrcLMzIwiRYowYcIE9YPVmDFjGDlyJCNHjgTAzs6OESNGsGnTJk6dOgVA48aNiY6OZuXKlSxfvhwTExM8PT3p27cvhoaGAIwePZo5c+awcuVKHj16RObMmfnpp5/o2bPnR3sJUkLBggVZtmwZkydPpl+/fiiKgr29PTNmzKBixYrJyutz62FgYIC/vz++vr4MHz4cAwMDateuTaZMmbh161aC9OXLl+fJkyfqIg0poX79+pibmzNnzhy6dOlCunTpKFOmDL169fqi+TIfO9+BAwfo0qUL3bt3T/IDcpNyz7/Pw8ODoUOHsnDhQrZv306WLFnw8PBg+vTpdOnSRV1AAOK+ZKhRowZ//vlnggeDW1lZsXLlSiZOnMjw4cOJjIzEzs4OX1/fTw69SsrvSoYMGVi8eDG+vr7069cPc3NzmjZtipmZmc7iKZ9zn5mamrJkyRImTpzItGnTePv2LXnz5k1S2d9XpUoVNm7cyIABA2jYsCHDhw9PNN2AAQOIjo5mypQpREVFYWNjw6+//srNmzfZu3evOlxw+vTpTJgwgWnTphEZGclPP/3Ezz//nKAn/l1t2rRh1KhRtG3bloULFybYn1L3c7169bh37x4BAQEsX74cKysrypUrR9OmTRkyZAg3b9785MOUIWnt/yndunUjS5YsLF26lDVr1pAxY0aqVatGz5491XmETk5Oajv/9ttvmJubU65cOfr06YORkREdO3bkxYsXLF68mBkzZpA9e3bq1KmDRqNhzpw5vHr1CgsLi292fYX4HmkUmWUohBDfjfhVzKpUqaL2XEDcKoPZs2dXHxALcRPna9WqhYeHB0OGDEmN4oqv5Pz587x8+VJn9b+YmBjKly9PjRo1PtlrJoQQ4uuTHi0hhPiOhIWF0aNHD5o2bUrlypWJjY3lr7/+4tKlS/Tt2xeIG1K6aNEiAgMDuX37NjNnzkzlUouU9uDBA3777Te6dOlCiRIlCA8PZ+XKlbx584aff/45tYsnhBAC6dESQojvzvbt2/H39ycoKAhFUXBycuLXX3+ldOnSwL89G1qtlv79+1OnTp1ULrH4GlasWMHy5cu5e/cuhoaGuLm50aNHD1xcXFK7aEIIIZBASwghhBBCCCFSnCzvLoQQQgghhBApTAItIYQQQgghhEhhEmgJIYQQQgghRAqTVQeT4OzZsyiKoj7HRwghhBBCCPHfFB0djUajwd3d/aPppEcrCRRFUf+JtEVRFKKioqRt0iBpm7RN2iftkrZJ26R90i5pm7TtR2qfpMYF0qOVBIaGhkRFRZE/f37MzMxSuzjiHWFhYVy5ckXaJg2StknbpH3SLmmbtE3aJ+2StknbfqT2CQwMTFI66dESQgghhBBCiBQmgZYQQgghhBBCpDAJtIQQQgghhBAihUmgJYQQQgghhBApTAItIYQQQgghhEhhsuqgEEIIIf4zYmNjiY6O/uzjIyMj1f/19OT76rRE2iZt+17ax9DQEH19/RTJSwItIYQQQvzwFEXh0aNHvHz58ovy0Wq1GBgY8ODBgzT9YfG/SNombfue2idjxoxYW1uj0Wi+KB8JtIQQQgjxw4sPsrJly4aZmdlnf4CKjY0lMjISY2PjFPvWW6QMaZu07XtoH0VRCAsLIyQkBIDs2bN/UX4SaAkhhBDihxYbG6sGWZkzZ/7ivABMTEzS7IfF/yppm7Tte2kfU1NTAEJCQsiWLdsXlTVt99sJIYQQQnyh+DlZZmZmqVwSIcT3IP694kvmc4IEWkIIIYT4j/jS+RZCiP+GlHqvkEBLCCGEECIZ0vpEfiFE2iDvFEIIIYT4z9Iq2mSl19fXx9TUNEXmmCT33F26dOHnn39OsL1JkyY4ODhw4sQJne3bt2/HwcGBR48efVE5P0RRFNavX8+zZ88+mMbBwUHnn5OTEx4eHnTs2JHr169/cRnmzp1LyZIlcXNzY/fu3V+cX1INGDCA5s2bf9VzeHt74+DgwMKFCxPdP3ToUBwcHPDz80tyni9evGDNmjVJTn/8+HEcHBy4d+9eko9JKX5+fnh7eyc5/b59+7h58yaQuuV+lyyGIYQQQoj/LD2NHttDt/M89vk3Pa+lviXVzKsl65hSpUoxevRoIiIiMDExAeDNmzdcuHCB7Nmzc/DgQUqUKKGmP3XqFHnz5sXa2jpFyx7v5MmTDBgwgD179nw03e+//85PP/0ExC3xHRISwh9//EGbNm3YuXPnZ8+de/36NZMmTaJDhw788ssvWFhYfFY+n2PQoEHq4g5fk6GhIdu3b6d169Y622NiYti5c2eyh7iNGzeOe/fu0ahRo5Qs5lfRpk0bmjVrlqS09+/fp1OnTixevJj8+fPj7u7O4cOHsbS0/Mql/DgJtIQQQgjxn/Y89jlPYp+kdjE+ydPTk+joaAIDAylevDgAf//9NxkyZKBRo0bs3LmTPn36qOlPnjyJl5fXVyuPoihJSpc+fXqyZs2qvraysqJ///40adKEo0ePUrFixc86/5s3b1AUhVKlSpEzZ05iY2OJiIj4rLySK3369N/kPJ6enhw6dIiHDx/qLDV+7NgxzMzM1BXykiqpbZYWmJubY25unqS079fLyMhI555LLTJ0UAghhBDiOxDfO3XmzBl126FDhyhVqhRlypTh6tWr6vN/Xr9+zfXr1yldujQAERERTJkyhYoVK+Li4kLdunV1htqtW7cOb29vfH19KVasGJ06dQLA39+fSpUq4ezsjLe3NzNmzEBRFI4fP06LFi0AqFixIuvWrUtWXQwM4r7rNzIyAuKCpiFDhlCyZEmKFi1KixYtCAwMVNP7+fnRuHFjevXqRZEiRahRo4Y6rKxly5bqz69evWLkyJGUK1cOV1dXmjRpwqlTpz6Yz4gRI1i3bh2VK1dm69ateHt74+rqStu2bXn8+DG+vr4UL16cUqVKMWfOHDWfd4cOxg9TO3DgADVr1sTZ2ZkaNWqwb98+NX1sbCyTJ0+mdOnSuLm50a1bN3x9fT85/NDV1ZUcOXKwfft2ne1bt26levXqCXq0zpw5Q7NmzXB1daV8+fKMGDGCt2/fqmVev349J06cwMHBAYi7T4YNG0a5cuUoVKgQXl5eDBs2LEHAeuDAAWrVqqXWbf/+/eo+RVGYN28eFStWxM3NjTp16rBp0yZ1f/z1uX//vrrt3r17ODg4cPz4cbVsXbt2pU2bNhQpUoQ5c+YkGDq4YcMGatSogYuLC2XKlMHX15eoqCju3bunBustWrTAz88vwdDBmJgYNT83Nzfq16/PwYMHP3rtU4IEWkIIIYQQ3wlPT0/Onj2rvj58+DBlypTB2dmZjBkzcujQIQBOnz6Nvr6+OpSwV69ebNiwgUGDBrFp0yYqVapE165ddYb93b9/n8ePH7N+/Xp69+7N3r17mT17NiNGjFB7y2bNmsWmTZtwd3dX5watWbNGHRr4KYqiEBwczPjx47GyssLd3R1FUWjfvj23b99mzpw5rF69msKFC9OkSRMuX76sHnv27FkyZ87Mxo0b8fPzU+ca+fn5sXbtWmJjY+ncuTOnTp1i7NixrF+/noIFC9KqVSudoO3dfFq2bAnAw4cPWbFiBTNnzmThwoUEBgZSu3ZtDAwMWL16NY0bN2bSpEkfnVc2fvx4Bg0axLp167C1taVPnz6EhoYCMGHCBFatWsXQoUNZt24d2bJlY8mSJUm6ZtWrV9cJtKKioti9ezc1atTQSXf16lVatWqFl5cXmzZtYsKECVy6dIk2bdqgKAqDBg2ievXq6rA6gP79+3PhwgWmTZvGjh07GDhwIOvWrWPVqlU6eS9evJjBgwezefNm7Ozs6Nmzp1q3yZMns3z5cnV/ixYtGD58OMuWLUtS/eLt2rWLUqVKERAQQO3atRPUbfDgwXTr1o0dO3YwatQoNm7cyPz588mePbvOvdCmTZsEeY8aNYply5bRp08fNm/eTLly5ejcubM6p+trkaGDQgghhBDfCU9PT0aNGoWiKAQFBfHo0SO8vLzQ09NTh5k1aNCAkydP4u7ujpmZGUFBQezZs4fZs2dToUIFALp27cq1a9eYPXu2ztC9zp07Y2trC8CRI0cwNjbGxsaGHDlykCNHDrJly0aOHDkwMjJS50RZWlqqc8YSM2zYMEaOHAnEPZcoJiaGQoUKMX36dNKlS8fRo0c5e/YsR48eVefU9OrVizNnzrB48WLGjBmj5tW9e3d12F58b4WFhQWWlpbs27ePK1eusHHjRgoWLAjELRhx/vx5/P39mTJlSqL5nDlzhujoaIYMGYK9vb16nc+dO0e/fv3QaDR07NiRGTNmcOPGDTXN+3r27Imnp6f6c506dbh+/ToFCxZk+fLlDBw4kCpVqgAwZMgQnYD5Y6pXr46/v786fPDIkSNkypQJJycnnXT+/v54enrSuXNnAOzs7Jg4cSKVKlXixIkTeHh4YGJigqGhoTqszsvLi2LFiqnXy8bGhqVLl3Lt2jWdvH///Xc8PDyAuEVZdu/eTVBQEPnz52fRokWMGzdOvbdy5crF/fv38ff3T/IcK4hrx3bt2iW67969e2g0Gp170d/fn3Tp0qGvr6/eNxYWFgmGG759+5bVq1czePBg9QuBHj16oNVq1WDxa5FAKxnk+RtCCCGESE2enp68fPmSf/75h8OHD1OwYEH1Q3Pp0qWZNGkSELcQRvywq/gPzUWLFtXJq1ixYkycOFFnm52dnfpz7dq1CQgIoEqVKjg4OODl5UXlypXJkSNHssrcvXt3NcDQ19cnU6ZMOh+GL126BJBgrlZUVBSRkZHq68yZM390btSNGzdIly4dBQoUULdpNBqKFSum9vR9LJ88efKoP5uammJjY6N+9jM2NgbQKc/78ubNq/6cLl06IC6wDAoKIiIigsKFC+ukL1q0KFevXv1gfvGcnZ2xtbVVF8XYunUrNWvWTJDu8uXLBAcH4+7unmBfUFCQGii9q2nTpuzdu5eNGzdy584drl+/zt27d3XuA9C9NhkyZADihqPevHmTyMhI+vfvz8CBA9U0MTExREVFJWvOXO7cuT+4r0yZMri7u9OgQQPs7OwoVaoUFStWxNnZ+ZP53rp1i+jo6ATX/7fffkty2T6XBFpJZGRklOwJh98rraJFTyOjSoUQQoi0Jlu2bOTPn5+zZ89y+PBhdQ4WxAVagwYN4uLFi1y+fJkhQ4Z8NC+tVqvOlYr3bs+UpaUlGzdu5OzZsxw5coTDhw+zYMECunXrRteuXZNc5syZM3/0Q7RWqyVdunSJzvOKn8P1ftkSoyhKol+Kv1/PD+VjaGio8zq5z0t7t6zvlin+3F+yEEX88MGmTZuyZ8+eRJdo12q11KpVS51f967EVt9TFIVOnTpx7do1atWqRdWqVenVq1ei901i10JRFLVOU6ZM0Qk04717Td6tf0xMTIK0H2tfY2NjFi9ezOXLlzl8+DCHDx9m5cqV1K1bl9GjR3/wOEjYrt+SBFrJkBrLv35rn7PcrBBCCCG+nfh5WqdPn9YZamVtbU3+/PlZuXIl5ubmFCpUCEAd6nb69Gl1eBfE9Xrlz5//g+fZuHEjb9++pVmzZhQtWpTu3bszePBgtm7dSteuXVNspI+9vT1v374lKipKpzdq8ODBFCxYEB8fnyTlU6BAAd68ecONGzfUoXAQV++P1fNry507NyYmJpw7dw5HR0d1+4ULFxINzhJTvXp15s6dy9q1a7G1tSVfvnwJ0hQoUIAbN27oBLX//PMP48aNo1evXqRPn16nzS5fvsyBAwdYvXo1bm5uQFwP3J07d9Tho5+SN29eDAwMePDggc69tXjxYm7evMn//vc/NdB58+YNWbJkASA4ODhJ+cc7cOAAgYGBdO3aFScnJzp06MCsWbOYPXs2o0eP/ui9mDt3bgwNDQkMDNS5Lxo2bEi1atU+OFwxJUiglQwpufzr/Yv32ThkI/fO30PfSB+HCg7U/aMu6TKnS5D2wOwDHJh9gNDnoVjmsqRav2q41Y77hdBqtQzIPQAU4J17bOTVkRibG3NgzgF2jt+JnoEe1fpXw6t13DKvsdGxTK48mebzmmNVwOqD5bSzs+PRo0cYGBig0WjQarVYWFjQrFkzxo4di56eHgsXLsTPz48bN26gp6eHq6srAwYM0JmkGb8izbx587hy5QqGhoY4OTnRsWNHddUiIYQQQnyap6enuox7kSJFdPaVLl2a1atXU758ebUXIn/+/JQrV44RI0YAcX/bt2zZwp49e3TmLb0vMjKSsWPHYm5uTrFixXj06BEnTpxQl5aPf/7V1atXEwwHTI4yZcrg6OhIz549GTx4MDly5GDlypUEBASwYMGCJOfj5eVFgQIF6Nu3L4MHDyZLliwsXbqU69evM2zYsM8qW0owNTWlefPmTJs2jaxZs5IvXz4CAgI4d+6cznPPPsbR0ZHcuXMzadIkOnbsmGia+OdODR06lBYtWhAaGsqIESMIDQ1VhwKamZkREhLC3bt3yZIlCwYGBmzbtg1LS0tevnzJ7NmzefLkCVFRUUkqV/r06WncuDFTpkzB3NycokWLcurUKcaPH0/79u2BuEDa3NycOXPm0LlzZ16/fs3kyZOTFagbGBgwY8YM0qVLR8WKFXn58iX79u1Th0nG34vXr19PMHfN1NQUHx8fpk6diqWlJQUKFCAgIICbN2/qBIdfgwRaqSAqPIo5P8/Bs7knHVZ1IPJtJMt+XcaKritov6K9TtrLuy6za/Iuuv3VDasCVpzfdJ5FbRYx+MxgMufKzOOrj4mNjmXsnbEYGOk2Z8SbCDYO3kifA31AgQnlJ1Ds52JxAdisAzhWcvxokBVv9uzZtGrVSn0dGBhIxYoVMTMzw8HBgYEDB7JhwwZKlChBTEwMK1eupH79+uzatYuyZcsC0KxZM44ePcrkyZOpXLkyRkZGbNu2jS5dunDgwAH8/f2//MIKIYQQn8FS/9s/1PRLzunh4UFUVBRlypRJ0CNSunRpFi1alOD5WZMnT2bSpEkMHjyY169fU6BAAfz8/KhcufIHz/Pzzz/z6tUrZs6cycOHD7GwsKBq1apqkGdvb0+5cuXo2bMnvXr1SnS1t6TQ19dnwYIFjB8/nt9++43w8HDy5cuHn5+furhEUhgYGDBr1iymTZtGt27diIqKolChQixatCjB/JxvrUePHkRHRzN48GDCw8OpUKECFStW/Oicr/dVr16dWbNmfXCFx8KFCzN//nymTp1K/fr1MTU1pWTJkvTv31+9T+rWrcuuXbuoWbMmu3btYsyYMfj5+bFs2TKyZs1K+fLladWqFXv27EnyUMeBAwdiaWnJtGnTCAkJwdramq5du9KhQwcgbr7ahAkTmDBhAo0aNSJPnjwMHDgwWT1JXl5e+Pr6smDBAiZPnoyJiQnlypVjwIABAGTKlIkGDRowbtw4goODE9zXvXr1wsDAgOHDh/P69WscHByYO3duoj2DKUmjfE9PLksl8UuCBuYOTJEercc3HrP+9/V0WNkBPf24b5subrvI0k5LGRM8Riftvhn72DN1D103d8XK3orArYEsab+EQScHkTFnRo4vO87hBYfpvad3gvNEhUUxIPcA+uzvg6IoTPSeyJjbYwh7Ecacn+fw2+7fMDLVfYPOqp+Vphmaqq/t7OwYPny4TqAFcd2tkZGR2NraEhQUxI4dO3T2jxw5EldXV+rUqcPGjRv5+eefuXLlSoLxu5cvX8bV1ZXNmzdTvXr1ZF/LsLAwrly5gqOj42c/WV58HdI2aZu0T9olbZPyIiIiuHXrFnny5EkwDyQ15yXLnOiUFf/AYhMTE/T19VO7ODp27dpF0aJFdeZKtWnTBmtra0aNGpWKJft20nL7vO9j7xnwb2zg4uLy0XykRysVWBWwotMa3YmK5zadw7ZwwvGwRRoU4cTyE4zxHBMXlGnAZ7YPGXNmBODO2TtEh0czseJEnt95jpW9FbWG1iKPRx6MzIxoML4Bc3+Zi0ZPQ+OpjTEyM2JZ52XUGFIjQZCVFNHR0Rw5coS9e/cyYsQIChUqRNWqValWrRo1a9akZMmSuLm56UykXL9+PV5eXolOknRycqJUqVKsWbPmswItIYQQ4kskN9CJjY0lKioKIyOjL/6wKEHWf4e/vz/Lly+nX79+pEuXjj179nDs2LFkDY0U3x8JtFKZoihsHbWVS9sv0W1LtwT7Y6NjyemSkyZ+TcjhnIPTa06zssdKrAtak8MpB4YmhuQumpvqA6tjlsmMw/MPM7vRbPod6kfm3JnxauWFV6t/hw9c3XuV2JhY8pbMy8JWC3nyzxPyeuSl3qh66Bsm/gejc+fO9OzZU31tY2ND79691Ymwp0+fZubMmUyZMoWgoCDSpUuHj48P48ePJ126dDx48ABra+sPXoMcOXLw4MGDz7+IQgghxDek1WpTuwjiOzNhwgTGjBlDq1atiIiIIH/+/EydOpWSJUumdtHEVySBViqKeB3B8q7LuXv+Lt22dCOHU8LnUgT0CyCPRx5yFckFgEczD06vPc2J5Seo+0dd6v5RVye9dzdvTiw/weWdlynTvozOvpjIGDYN30S7Ze3YNWkXZpnM6HugL3MazeHY0mPqQhnvmzlzZoKhg+9ydXVl9uzZADx58oTdu3fTr18/3r59y5IlS8iePftHn7x969atBBMXhRBCCCF+FDY2NkyfPj21iyG+MemzTiVPbz1lYqWJRLyJoPfe3okGWQAv7r0gJlL3WQP6hvroG8X1Pm35Ywv3LtzT2R8TFYOhScJnBuz124t7PXcsbS15eOUhtm62cU/ZdrPh4eWHn1WPXLlyMWPGDPV11qxZadKkCf369VOfeN6oUSNOnjypjmd919mzZzlz5gwNGjT4rPMLIYQQQgiRFkmglQrCXoYxo84M8hTPQ6eAToku6R7PuZozh+Yf4u75u2i1Ws5tPMfNwzdxrxe3nOXDKw9ZN3Adrx+/JiYyhu3jthPxJgKXmrqT857decaFvy5QoUvcMpZZ82Xl9snbxEbHcufMHbLkyfJZdfHx8eGPP/7gr7/+4tWrV8TExHD+/Hn8/f3V4KlmzZo0a9aMWrVqsWnTJkJDQwkNDWXjxo3UqVOHJk2aJPqEcyGEEEIIIb5XMnQwFRxfdpwX915wbuM5zm06p7Nv3N1x9LPtx8+TfqZYo2JU7V8Vjb6GhS0XEvYijKz5stJmSRtsXGwAaDK9CRsHb2Rc2XFEhUWRu0huOq/vjHkm3WdZrBuwjtr/q60uAV+pZyX+bPsng/IPwr6cPaVal/qsuvj6+pIjRw6GDx/OtWvX0Gq15MmTh3bt2tGjRw813cKFC1mwYAGjR49Wn5vl5OTEiBEjPjosUQghhBBCiO+RLO+eBCm9vHta9v7y7mmdLIOcdknbpG3SPmmXtE3K+9RSzcnxPS1R/V8jbZO2fU/tk1LLu8vQQSGEEEIIIYRIYRJoCSGEEEIIIUQKk0BLCCGEECIZ9PTk45MQ4tNkMYxksDO0I5NeptQuxldloW+R2kVIFo1Gg6mpKRqNJrWLIoQQ4jukaLVokhE46evrY2pqmirnft/bt2/x8vLC3Nyc/fv3Y2RklCLlSmu8vb25f/8+AwYMoHXr1gn2Dx06lFWrVtG5c2fatWuXpDxfvHjB7t27adSoUZLSHz9+nBYtWrBnzx5sbGySVf4v5efnx/r169m7d2+S0u/btw9bW1vy58+fquUWEmglSynTz1uZT3w9pqamP9TDjr/0j64QQojk0ejpEbNuGcqTx9/2vFmtMKjf7Ivy2LJlC5kzZ+bp06fs2rWLGjVqpFDp0h5DQ0O2b9+eINCKiYlh586dyf7Cddy4cdy7dy/JgVZqatOmDc2aJe1euX//Pp06dWLx4sXkz58fd3d3Dh8+jKWl5VcupUiMBFrJkBpvxOK/IyX+6AohhEg+5cljeHT/254zBfIICAigdOnSPH78mJUrV/7QgZanpyeHDh3i4cOHZM+eXd1+7NgxzMzMkt3L+D0tum1ubo65ufmnE5KwXkZGRmTNmvVrFEskQZoKtGbOnMnRo0dZsmSJuu3KlSv4+vpy8eJFMmbMSPPmzWnbtu1H89m2bRt+fn7cvXsXOzs7+vbtS9myZb+4fKnxRixS3vILV+iyebfOtqjYWDQaDW+H9PjAUXDx8VO85i1nU7N6lMtjC0B4dDR9th9g09WbRMbG4p49G+OrlsfVOu5NbfqxM/geOIaBnh5DynvSobgbANGxsXjNW8GShj/hkCXuW6bv5y1fCCFEagsKCuL8+fO0bduWsLAwBgwYQFBQEPny5VN/XrNmjZr+0aNHVKhQgQULFuDp6cmZM2eYOHEigYGBWFpaUqFCBXr37k26dOmAuOF6lSpV4vDhwzx79oypU6fi5OTExIkT2b9/P0+fPiVjxoxUqlSJgQMHqktgX7x4kdGjR3Px4kWyZMlCjx49GDBgAAsXLsTDwwNFUZg/fz4rV67k6dOn2NnZ0bZtW2rXrv3R+rq6uhIUFJSgV2vr1q1Ur16dbdu26aT/WP0GDBjA+vXrAXBwcODatWu8fv36k3UDOHDgACtXruTWrVvkzp2bvn37Ur58eYBP1i2xYXz37t2jYsWKLF68GA8PDwYMGMDbt28JCwvj3LlzdOzYkaioKJ2hgxs2bGDevHncuXOHjBkzUq1aNfr27UtISAgVK1YEoEWLFnTt2pUSJUronDMmJoZZs2axfv16nj17Rr58+ejZs2eKfE4WCaWZMUqLFi1i2rRpOttevHhB69atsbOzIyAggG7dujF16lQCAgI+mM+xY8fo27cvTZs2ZcOGDZQuXZouXboQFBT0tasgvhNNXR15Maib+u9it1ZkMTNlTu3KHzwmLCqa5gFbCI+J0dn+v/1HufHsBee7tORen064WmXl51WbAHgTGUXfHQfY2aoRW5o3oMfWvYRGRQMw7dgZqhawU4MsIYQQIjnWrl2LmZkZZcuWpVKlShgZGbFixQoA6tWrx4ULFwgODlbTb9q0CSsrKzw8PLh69SqtWrXCy8uLTZs2MWHCBC5dukSbNm10ekRWrFjB4MGDmT9/PkWKFKF///5cuHCBadOmsWPHDgYOHMi6detYtWoVAI8fP6Zly5bkzJmTgIAAhg4dyuTJk4mNjVXznDx5MsuXL2fw4MFs3ryZFi1aMHz4cJYtW/bJOlevXp3t27err6Oioti9e3eCnrxr1659tH6DBg2ievXq6rA64JN1i7d48WK17HZ2dvTs2ZPQ0NAvrtu7du3aRalSpQgICEgQgF69epXBgwfTrVs3duzYwahRo9i4cSPz588ne/bsanDt5+dHmzZtEuQ9atQoli1bRp8+fdi8eTPlypWjc+fO3Lx5M1llFEmT6j1ajx8/ZtCgQZw+fZo8efLo7Fu9ejVGRkYMHz4cAwMD8uXLR3BwMPPmzaNBgwaJ5jdv3jwqV66Mj48PEPeLc/bsWf7880/+97//ffX6iO+Loii0Xred6vZ5aOb24ble3bbsoU7B/FwKeaaz/eqT52gVBQVQUNDX02BqGPdrpf//48UVJa63SqPRoAHuvXrD0vOX+bv99/NgaCGEEGlHTEwMmzdvpkKFCuqQuXLlyrFx40Z69+5NiRIlsLW1ZfPmzXTt2hWAzZs3U6dOHfT09PD398fT05POnTsDYGdnx8SJE6lUqRInTpzAw8NDzbNUqX/np3t5eVGsWDEKFiwIgI2NDUuXLuXatWsArFq1igwZMuDr64uhoSH58+dnyJAh/Prrr0Dcw7gXLVrEuHHjqFChAgC5cuXi/v37+Pv7f3IeUvXq1fH391eHDx45coRMmTIlmKsd32v3sfqZmJhgaGioDqv7VN3i/f777+r16dKlC7t37yYoKIj8+fN/Ud3eZWFh8cFFPe7du4dGo8HGxoYcOXKQI0cO/P39SZcuHfr6+upcLAsLiwTDDd++fcvq1asZPHgwP/30EwA9evRAq9WqwaJIWakeaF26dAkLCws2bdrEjBkzuH//36F5p06donjx4hgY/FvMkiVLMmfOHJ49e0bmzJl18tJqtZw5c4YBAwbobPfw8GDXrl1ftyLiu7TswhUuP3lGQJM6H0yz5Nxlgp6/ZG6dKow6eFxn32+livLLqs1kHzcLfY2GLGam7GwVN7HWzMiQaTUqUnf5evQ0GmbXroyZkSFtNmxnZMXSmBoaftW6CSGE+DEdOHCAJ0+eqB+WAX766Sd27drFli1baNiwIXXr1lUDrStXrnD9+nV15NDly5cJDg7G3d09Qd5BQUFqIJE7d26dfU2bNmXv3r1s3LiRO3fucP36dXWaRny+hQoVwvCdv2/FihVTf7558yaRkZH079+fgQMHqttjYmKIiooiIiJCZ5je+5ydnbG1tVWHD27dupWaNWsmSHf58mXu3Lnzyfolp27x3u0UyJAhAwARERFJqltSvX/d31WmTBnc3d1p0KABdnZ2lCpViooVK+Ls7PzJfG/dukV0dDSFCxfW2f7bb78luWwieVI90PL29sbb2zvRfY8ePcLe3l5nW7Zs2QB48OBBgkDr9evXhIWFYW1tneCYhw8fpmCpxY9Aq1UYdeAYA8qUIL1x4kviXn3ynGF7D7O/TWP0E1kNMEarUM+pAIPKlSSDsTEDdh6kwYqNnPm1BSaGBrQv5kr7Yq5q+l03bxMdq8UrV04ar97MzWcvKZUrBxOrlUfCLiGEEEmxbt06ALp3755g38qVK2nYsCH16tVj+vTpXLhwgW3btuHu7q4GCVqtllq1atGpU6cEx7+7Ot27QY+iKHTq1Ilr165Rq1YtqlatSq9evRgyZIiaRl9fH61W+8Fyxw9LnDJlCnnz5k2wPynL08cPH2zatCl79uzRmYf27nmSUr/k1C1eYs9QUxQlWXV7d3hmzHtTEoCPBpvGxsYsXryYy5cvc/jwYQ4fPszKlSupW7cuo0eP/uBxgE4ALL6NVA+0PiYiIiLBL52xsTEAkZGRiaaHhL+oxsbGiaYX/237b9/l0dtQWhdxSXR/RHQMzdb+xYRq5cmVMUOC/dGxsTRZvZlNzeqRM0N6AKb8VIGsY2aw+59gajrk00kfGRPDwF2HCGhSh7GHjmNpasLJTj7UWrqOhWcu0iFnLgDCw8O/q9WQPiQ8PFznf5G2SPukXdI2KS8yMhKtVktsbKzOfCGICw5S0/vl+ZTnz5+zf/9+6tWrR6tWrXT2LV68mICAAAIDA3FycqJEiRJs27aNrVu30qlTJ/Vc+fPn58aNGzrPVbp16xbjx4/nt99+o0CBAiiKol4ziOslOnDgACtWrMDN7f8XdoqOJjg4GBsbG2JjY7G3t2fdunVERESoH+rPnj0LxAV3uXPnxsDAgHv37uksvrBkyRKCgoIYPnx4otfj3bJUqVKFuXPnsnr1amxsbLCzsyM2Nlbn72ZS66coCrGxsUmqW3wA+e41eXdbUuoWf6+9evWKHDlyqOV6N993yxVPq9Wq2w4ePMjFixfp3LkzDg4OtG3bltmzZzN37lz++OOPBOV897WNjQ0GBgZcuHCBAgUKqPn/8ssvVKlS5ZOLzX2p+DZ6v35pUfy1Cw8PT/TLA0VRkvRIgTQdaJmYmBAVFaWzLT5gMjMzS5A+PghL7JiUerig+HGsv3yDOgXzY26U+Dc8px484sazF3TcuIuOG/8delp3+QZ83Jz4X0UvXkREEvnOm4W+Rg89jQajRP5wTzxyikbODuTOmIFLIc+oUzA/Go2GwtmtuBjyVE1369atH+oD1u3bt1O7COIjpH3SLmmblGVgYJDgS1c9Pb1U/3wQFRX10V6g961bt47Y2Fh8fHywtbXV2deqVSs2bNjAsmXLGDJkCDVq1GDs2LHExMRQoUIF9Qvppk2b0q5dO4YMGUKTJk0IDQ1l9OjRhIWFYWVlRUREBIqiEBMTox6TPn16DAwM2LJlC+nSpePVq1f4+/vz9OlTwsPDiYiIoH79+vz5558MHjyYli1b8uTJE3x9fYG4wMXQ0JAGDRowdepUjIyMKFy4MGfPnmXixIm0bNnyg8Pr3i1Lnjx5yJUrF1OmTKF169bqMfFpklo/Y2NjHj9+TFBQUJLqFv/ZMjIyUj1n/P0UFRWVpLrlypULc3NzZs6cSbdu3Xjy5AmTJ09Go9GowwvjP+C/ey1iYmJQFIWIiAi0Wi0zZ87EyMiIChUq8OrVK/bu3YurqysRERFqMHf58mXy5MmjU26NRkPjxo2ZOnUq6dKlI2/evGzatIkbN24wbNiwZA1v/BLfQ+dHZGQkMTEx/PPPPx9Mk5Qe2DQdaFlbWxMSEqKzLf61lZVVgvQZM2bEzMws0WPeH04oxJE79+nqkXD8drzSuW14PVh3uXej4ZPY0LSuury7V66c/L7rEOua1CWDsRHD9x0hi5kpXrly6hx3+8UrNly5yeF2TQDIb5mRY/ce0tK9EKfuP6K6/b9jvvPkyfPD9Gjdvn0bOzu7VP8gIxKS9km7pG1SXmRkJA8ePMDY2DjRYVmarFbf/BEbmqxxn2OS8mHtXZs3b8bT0xNHR8cE+/Lly0elSpXYvn07AwcOVAOtihUr6jxLqUSJEsydOxc/Pz+aNWuGqakpHh4e9O3bV513pNFoMDAwUK+Xra0to0aNYvr06axevZosWbJQvnx5WrZsyd69ezE2NiZHjhzMnTuXMWPG0KRJE6ytrWncuDETJkzA3NwcExMTBg0aRLZs2ZgzZ476+axz5860b9/+gz0E75elevXqzJ49m9q1a6vb4tMAFC9e/JP1a9CgAfv376dRo0bq6n0fq1t8O717D8V/wW9kZJSkupmYmDBu3DgmTZpEo0aNyJMnD/369aNjx45qHvr6+ujp6encpwYGBurx5cuXZ+TIkSxcuJCZM2diYmJC2bJl6devHyYmJlhbW1O/fn2mTJnC/fv3qVy5sk65+/Tpg4mJCaNHj+b169fY29szZ86cBAuKfA2KohAZGYmxsXGyHzCdGgwMDMiVK5fazu9K6iqNGiUNfaIbMGAA9+/fV5+jNXfuXFauXMmuXbvUCH3ixIns2rVLZ3nPd7Vt2xZLS0vGjx+vbmvcuDH29vafvepgYGAgAAX/3iXP0fqBZPL1Y8XPNalWIE+C7TNqVaKpa8I/YkbDJ7GrZSM10Hr8NpQBOw+yOyiYaK0WD5vsjK9aHvssmXSOq7d8A91LFqFC3rjhgQ/fvKXZmi1cePwE77y5+LN+dUxt7TDs2Osr1fbbCwsL48qVKzg6OibaAy1Sl7RP2iVtk/IiIiK4desWefLkSRBoKVotmkTm3XwLqXnur+HmzZu8evWKokWLqtvOnDlDkyZN2L9/v86Dhr+G2NhYdUGN1B4SKhL6ntrnY+8Z8G9s4OKS+PSTeGm6R6tBgwbMnz+fQYMG0a5dOy5cuMCff/7JiBEj1DRv3rwhOjpandzYunVrOnTogJOTE2XLliUgIEB96LEQ73oxqFuytgNEDdcNhKzSmbOwfvVPnmt907o6r7OnT8feNr98upBCCCG+quQGOrGxsURFRWFkZPTFHxZ/pCAL4h7Z06FDB3x9fSlevDghISGMHj2aEiVKfPUgS4i0KE3/hmfOnJn58+dz69YtdfWcfv36Ua9ePTWNr68vDRs2VF+XLl2aUaNGsWLFCurVq8exY8eYPXs2+fLlS+wUQgghhBDJkpw5Vf8lXl5eDBo0iDlz5lCtWjW6dOmCg4MDfn5+qV00IVJFmurRGjNmTIJtrq6uCZ7K/alj6tatS926dVOyaEIIIYQQ4hOaNm1K06ZNU7sYQqQJaSrQSus0BQqiZMmW2sUQPyhNpoTP9hBCCCGEEN8nCbSSwcD7p08nEuI7Easo6H8Hq/4IIYQQQnyPJNBKBt+nwQRHp/21/4X4lNyGxgzKkju1iyGEEEII8cOSQCsZgqMjuRH14zxIVnx9by5e4eb/xvP2ynX0TUzIWqMy+Qb+hp6x7nNTLrTswsuTZ3S2acPCyd6kAQ6jhwBwf8lq7s5fQtSTp5ja5iRPv+5kqRj39Pl7C5dze9pcNPr62PXsRE6fRnF5REdzpm5znKaNwSyf3QfL+eLFCwYNGsTmzZt5/vw5GTJkoEqVKvj6+mJjY4NWq2XChAksWrSIO3fuYGhoiIeHB8OGDcPT01Mnr5iYGOzs7IiKimLDhg1feAWFEEIIIb5PaXrVQSG+Z4pWS2Cb7mStXonS5w9SZNMynh88yp05ixKkdf1zBmUvH1X/FRjeH+Mc1tj91gmAR2s3cXvqHJymjqLMpb/J1bktl37tTeTjEGLehnLzj4kUXjEPtyWzuDFsDLFhcV8I3FuwDMvyXh8NsgB++eUXnj59ysmTJwkNDeXcuXNERkZSuXJlYmJiGDNmDIsWLWLt2rW8efOG+/fv4+3tjbe3d4KH9gUEBJAzZ05y5szJ1q1bU+RaCiGEEEJ8byTQEuIriXn1mqiQJyhaLfz/c8E1enromyZ88N27woJuc2PoGBynjMI4W1YA7s5bTJ7enclQ2AWNRoNVneoUWbcY/XTp0Oj//6+xooCixD1tXQMRDx/zeN1f5O7W/pNlPXz4MPXq1cPa2hoAKysrpkyZgoeHBy9evODw4cOUKVMGJycnNBoNZmZm9OvXjw4dOvD48WOdvKZPn84vv/xCu3btWLZsGWnomehCCCGEEN+MBFpCfCWGmTJi09aHIN9JHLAvwTHPqpjmyYVNW5+PHnd9yCisG9QiY4kiAMSGhxN6PQiNvj5nf27D4cLlOFO/BbFh4RiYm6Fvaor9/wYS2KYbge174jBmKPqmpgSNHE+evt3QT+SJ5u9r0qQJnTp1onPnzqxevZrg4GCsra1ZtGgRWbNmpXHjxvj7+9OwYUPmz59PYGAgWq2WqVOn4uXlpeZz/vx5zpw5Q+vWrfnll1948eIF27dv/7ILKYQQaYxeKj5o2NvbGwcHB/Wfs7MzVatWZf78+Sl6HkVRWL9+Pc+ePftgmnfL4eDggJOTEx4eHnTs2JHr169/cRnmzp1LyZIlcXNzY/fu3V+cX1INGDCA5s2bf9VzxLfjwoULE90/dOjQZD+D7MWLF6xZsybJ6Y8fP46DgwP37t1L8jEpxc/PD29v7ySn37dvnzqCJjXLnVwSaAnxlShaLXomxhQYMYCyl49SfOdawm7+w+3Jsz54zMuTZ3l99gK5e3RUt8W8eg2Kwt25i7H/43dKndhNtjo/caFVF8Lv3gcgR7OGeB7dgeeRbVg3rM3zg3+jjY7Borg7lzr34WT1n7k+ZBTa6OhEzztv3jxmzJjBnTt36NChA3Z2duTPn59ly5YB0KJFC/bt24eZmRkjRozA1dWVrFmz8vvvvxMTE6Pm4+fnR4sWLciUKRNmZmbUrVuXadOmpcTlFEKIryI2mb3u+vr6mJqaoq+v/83PHa9NmzYcPnyYw4cPs23bNrp168b06dPV9+yUcPLkSQYMGEB4+Mfnpv/+++9qWfbt28f8+fN5+fIlbdq0ISws7LPP//r1ayZNmsTPP//M1q1bdb7U+9oGDRr0TR6ybGhomOiXkTExMezcuTNuhEoyjBs3jk2bNqVU8b6qNm3asHbt2iSlvX//Pp06dVKDfnd3dw4fPkz27Nm/ZhFThCyGIcRX8nTHXp5s24PH3g0AmNvnx65HR24MH0ee3l0SPebh8rVkq1EF42xZ1G0ao7iFM2za+WBunz/u55aNebB0Nc/3HyZn81908tBGRhE0egou86ZwZ8Z8DCwsKLZ1FRdaduHhqg3qQhnv0tPTw8fHBx8fHxRF4cqVKyxZsoTmzZtjbW1NxYoVKVOmDGXKlAHi3vS2bNlCv3790NfXZ+TIkTx//pzly5ejr6+vfqMWERFBaGgop0+fpmjRol92QYUQ4ivQ12hSZVXhL1n91czMjKxZs6qvbW1tOX78OAEBATRr1ixFypfUYd/p06fXKYuVlRX9+/enSZMmHD16lIoVK37W+d+8eYOiKJQqVYqcOXN+Vh6fK3369N/kPJ6enhw6dIiHDx/qBA3Hjh3DzMwMU1PTZOX3PQ3VNzc3x9zcPElp36+XkZGRzj2XlkmPlhBfScT9hyhRUTrbNAYG6BkaJppeGxPD0137sapfU2e7kWUmDLNYJshLif137te77sz9k2w1q2Jik4PQa0Gkd3FEo9GQ3rkgodduJEi/Y8cO0qVLx/Pnz+PKqNHg5OTE6NGjcXd35+jRo5ibm/PXX3+px+TMmZMOHTrQtm1bzp49C4C/vz/58+fnypUrnDt3jqNHj7J69Wq8vb2ZOHFiEq6YEEKkjvhVhb/lv5QO7N7/UK4oCvPmzaNixYq4ublRp06dBL0d/v7+VKpUCWdnZ7y9vZkxYwaKonD8+HFatGgBQMWKFVm3bl2yymJgEPc9vtH/f1H45s0bhgwZQsmSJSlatCgtWrQgMDBQTe/n50fjxo3p1asXRYoUoUaNGuqwspYtW6o/v3z5khEjRlCuXDlcXV1p0qQJp06dUvOZPn06rVu3pk+fPhQpUoQRI0awbt06KleuzNatW/H29sbV1ZW2bdvy+PFjfH19KV68OKVKlWLOnDlqPu8OHYwfpnbgwAFq1qyJs7MzNWrUYN++fWr62NhYJk+eTOnSpXFzc6Nbt274+vp+cvihq6srOXLkSNCrtXXrVqpXr56gR+vMmTM0a9YMV1dXypcvz4gRI3j79q1a5vXr13PixAkcHByAuF7BYcOGUa5cOQoVKoSXlxfDhg0jIiJCJ98DBw5Qq1YttW779+9X933qPkpsGN+9e/dwcHDg+PHjatm6du1Ku3btKFOmDHPnzk0wdHDDhg3UqFEDFxcXypQpg6+vL1FRUdy7d08N1lu0aIGfn1+Cc8bExKj5ubm5Ub9+fQ4ePPjRa/+tSKAlxFdiWa4UkSFPCZ4xHyU2lvA79wiePh+reok/+Dr06g20EZFkKOKWYF+Opg25PW0uby5dRRsTw72Fy4l6HEKWKhV00oXfvc/T7XuwbR/3B9I0Ty5en7mANjqa1+cvYZrbNkHeZcuWxcrKitatWxMYGEh0dDRv3rxh2bJl3LhxgwYNGlCnTh369u3L/v37CQ0NJSoqiiNHjrB27VoaNGiAVqtl5syZtGnTBhsbG2xsbMiZMydWVla0a9eONWvWcOfOnRS4qkIIId534cIFNm/ezC+//DvCYfLkySxfvpzBgwezefNmWrRowfDhw9XhhXv37mX27NmMGDGCnTt30qdPH2bNmsWmTZtwd3dXh86tWbOGn35K/O/W+xRFITg4mPHjx2NlZYW7uzuKotC+fXtu377NnDlzWL16NYULF6ZJkyZcvnxZPfbs2bNkzpyZjRs34ufnp46M8PPzY+3atcTGxtKmTRtOnTrF2LFjWb9+PQULFqRVq1Y6Qdv58+fVfFq2bAnAw4cPWbFiBTNnzmThwoUEBgZSu3ZtDAwMWL16NY0bN2bSpEkfnVc2fvx4Bg0axLp167C1taVPnz6EhoYCMGHCBFatWsXQoUNZt24d2bJlY8mSJUm6ZtWrV9cJtKKioti9ezc1atTQSXf16lVatWqFl5cXmzZtYsKECVy6dIk2bdqgKAqDBg2ievXq6rA6gP79+3PhwgWmTZvGjh07GDhwIOvWrWPVqlU6eS9evFi9T+zs7OjZs6dat0/dR0m1a9cuPD09Wbp0KbVq1UpQt8GDB9OtWzd27NjBqFGj2LhxI/Pnzyd79uw690KbNm0S5D1q1CiWLVtGnz592Lx5M+XKlaNz584JVkVODTJ0UIivxLxAPlwWTOPWhBncmb0Igwzpsar7E3Y94pZsP+jkicOowVjVjXszDb9zD4OMGdA3MU6Ql13PThikT8flbv2JfBSCef48uCycjrG1lU66myPGkW9QL/SM4nrNcnVuw+Wu/TlSpAKZvDzIkciwQVNTUw4fPszw4cOpVasWISEhGBkZ4enpya5du3B0dGThwoWMHz+ebt26cevWLQAcHR3x9fXFx8eHTZs2cf/+/USHrNSoUYOsWbMyZcoUJk2a9GUXVQghBHPmzGHBggUAREdHEx0djZubmxoQhYWFsWjRIsaNG0eFCnFfyOXKlYv79+/j7+9Ps2bNuHPnDsbGxtjY2JAjRw5y5MhBtmzZyJEjB0ZGRlhYWABgaWmJyUcWVRo2bBgjR45UyxITE0OhQoWYPn066dKl4+jRo5w9e5ajR49iaWkJQK9evThz5gyLFy9mzJgxal7du3dXh+3F91ZYWFhgaWnJgQMHuHTpEps3b8be3h6IWzDi/Pnz+Pv7M2XKFDWfrl27kjFjRiCuFyg6OpohQ4aox3l6enLu3Dn69euHRqOhY8eOzJgxgxs3bqhp3tezZ0/1uZE9e/akTp06XL9+nYIFC7J8+XIGDhxIlSpVABgyZIg62uNTqlevjr+/vzp88MiRI2TKlAknJyeddP7+/nh6etK5c2cA7OzsmDhxIpUqVeLEiRN4eHhgYmKCoaGhOqzOy8uLYsWKUbBgQQBsbGxYunQp165d08n7999/x8PDA4AuXbqwe/dugoKCyJ8//yfvo6SysLCgbdu2REREJLif7t27h0aj0bkX/f39SZcuHfr6+up9Y2FhkWC44du3b1m9ejWDBw9W7/8ePXqg1WrVYDE1SaAlxFdkWboklqVLJrqv7OWjOq+z/VSZbD9VTjStRk8P2/Yt1J6qD3GZP1XntXG2rLivXvDJcmbPnl1n2MT7jI2NGTx4MIMHD050f+3atYl6b2hjPAMDAx48ePDJMgghhEiaxo0bq8PSYmJiuH37NpMnT6Zp06YEBARw8+ZNIiMj6d+/PwMHDlSPi4mJISoqioiICGrXrk1AQABVqlTBwcEBLy8vKleuTI4cOZJVlu7du6sBhr6+PpkyZdL5MHzp0iWABHO1oqKiiIz8d/hk5syZPzo36vr166RPn14nENJoNBQrVoxDhw6p2ywtLRPNJ0+ePOrPpqam2NjYqEPzjI3jvuB8tzzvy5s3r/pzunTpgLjAMigoiIiICAoXLqyTvmjRoly9evWD+cVzdnbG1taW7du307p1a7Zu3UrNmjUTpLt8+TLBwcG4u7sn2BcUFKQGSu9q2rQpe/fuZePGjdy5c4fr169z9+5d7OzsdNK9e20yZMgAxM2xTsp9lFS5c394PmKZMmVwd3enQYMG2NnZUapUKSpWrIizs/Mn87116xbR0dEJrv9vv/2W5LJ9TRJoCSGEEEJ8RywsLHQ+uObLlw8LCwuaNWvG33//TaZMmQCYMmWKToAQz8jICBMTEzZu3MjZs2c5cuQIhw8fZsGCBXTr1o2uXbsmuSyZM2f+6IdorVZLunTpEp3nFT+HC/horxnEDUtMbBU+rVarzgmDf4Om9xm+Nz86uUv0v1vWd8sUf+4vWYgifvhg06ZN2bNnT6JLtGu1WmrVqkWnTp0S7Ivv8Xm/bJ06deLatWvUqlWLqlWr0qtXL4YMGZIgbWLXQlEUtU4fu4/eTR/v3dWI432sfY2NjVm8eDGXL19WV7BcuXIldevWZfTo0R88DhK2a1ojc7SEEEIIIX4QWq2WvHnzqqMJcufOrf47cOAA/v7+6OnpsXHjRlasWEHRokXp3r07q1evplGjRmzduhUg2UuLf4i9vT1v374lKipKpyzz5s1jz549Sc7HwcGB169fJ5hHdfr0afLnz58iZf0cuXPnxsTEhHPnzulsv3DhQpLzqF69OufPn2ft2rXY2tqSL1++BGkKFCjAjRs3dK5hbGwso0eP5uHDh4Bum12+fJkDBw4wbdo0+vTpQ+3atcmVKxd37txJclCYlPsoPtCJX5QDIDg4OMl1h7jFOKZPn46TkxMdOnRg8eLFdO/ePUn3Yu7cuTE0NNSZpweoz/1MbdKjJYQQQgjxHQkLC+PJkydAXE/CnTt3GDVqFNmyZcPT0xNTU1MaN27MlClTMDc3p2jRopw6dYrx48fTvn17IG6Y3NixYzE3N6dYsWI8evSIEydOULx4cSBuCXmIW6jg/eGAyVGmTBkcHR3p2bMngwcPJkeOHKxcuZKAgAB1nllSeHl54eDgQO/evRk8eDBZsmRh6dKlXL9+nWHDhn1W2VKCqakpzZs3Z9q0aWTNmpV8+fIREBDAuXPnKFGiRJLycHR0JHfu3EyaNImOHTsmmqZNmzY0a9aMoUOH0qJFC0JDQxkxYgShoaHqUEAzMzNCQkK4e/cuWbJkwcDAgG3btmFpacnLly+ZPXs2T548+eBQ//elT5/+k/eRvb095ubmzJo1i969exMSEsLkyZOTFagbGBgwY8YM0qVLR8WKFXn58iX79u1Th0nG34vXr19PMHfN1NQUHx8fpk6diqWlJQUKFFCHz8bPK0tNEmglQ27DxLujhfjeyL0shBD/So33xC8554IFC9QgRU9Pj0yZMlG0aFEmTJigLvM+cOBALC0tmTZtGiEhIVhbW9O1a1c6dOgAwM8//8yrV6+YOXMmDx8+xMLCgqpVq9KnTx8g7gN0uXLl6NmzJ7169Up0tbek0NfXZ8GCBYwfP57ffvuN8PBw8uXLh5+fn7q4RFIYGBiwcOFCxo4dS7du3YiKiqJQoUIsWrQowfycb61Hjx5ER0czePBgwsPDqVChAhUrVvzonK/3Va9enVmzZn1whcfChQszf/58pk6dSv369TE1NaVkyZL0799fHcJXt25ddu3aRc2aNdm1axdjxozBz8+PZcuWkTVrVsqXL0+rVq3Ys2dPknu1PnUfpUuXjgkTJjBx4kRq1KhBnjx5GDhwIO3atUty3b28vPD19WXBggVMnjwZExMTypUrx4ABAwDIlCkTDRo0YNy4cQQHB1O5su589l69emFgYMDw4cN5/fo1Dg4OzJ07N9GewW9No3xPTzdLJfHdkS4uLqlcEiFSTqyioJ9CQ0MSExYWxpUrV3B0dFS/jRJph7RP2iVtk/IiIiK4desWefLkSTBX5Gu/F35Map77RxQbG6uuaqevr//Nzrtr1y6KFi2qM1eqTZs2WFtbM2rUqG9WjrQutdrnc3zsPQOSHhvIHK0kioqKIjw8PLWLId4THh7O5cuXpW0+g/xxF0KI5L8XxsbGEh4eTmxs7Dc/t0ib/P396d27N1euXOHu3bssWrSIY8eOUbt27dQumkhlEmglg3T+pT2KohAeHi5tI4QQ4pvRarWpXQSRhkyYMAFzc3NatWpFzZo12bx5M1OnTqVkycQf7yL+O2SOlhBCCCGEEJ/JxsaG6dOnp3YxRBokPVpCCCGEEEIIkcIk0BJCCCGEEEKIFCaBlhBCCCH+E2Q+rxAiKVLqvUICLSGEEEL80AwNDYG4pfOFEOJT4t8r4t87PpcshiGEEEKIH5q+vj4ZM2YkJCQEADMzMzSfubR6bGys+iDatP4soP8aaZu07XtoH0VRCAsLIyQkhIwZM35xOSXQEkIIIcQPz9raGkANtj6XVqslJiYGAwMD9PRkYFBaIm2Ttn1P7ZMxY0b1PeNLSKAlhBBCiB+eRqMhe/bsZMuWjejo6M/OJzw8nH/++YdcuXJhamqagiUUX0raJm37XtrH0NAwxXrcJNASQgghxH+Gvr7+F32Iin9YsbGxMSYmJilVLJECpG3Stv9i+6TtfjshhBBCCCGE+A5JoCWEEEIIIYQQKUwCrWT43BWKhBBCCCGEEP8tEmglkZGRUZqeuJdWaRVtahdBCCGEEEKIb04Ww0iG7aHbeR77PLWL8d2w1Lekmnm11C6GEEIIIYQQ35wEWsnwPPY5T2KfpHYxErh+8Dp//e8vHl9/jJGZEYXrFKbW8FoYmRolSHtg9gEOzD5A6PNQLHNZUq1fNdxqu6n790zbw8E5Bwl/FY6tuy0/T/oZqwJWccfOOcDO8TvRM9CjWv9qeLX2AiA2OpbJlSfTfF5zNW1iNBoNJiYm6OvroygKRkZGlC1blunTp2Nra6uT9tKlS7i4uFCrVi02btwIwJ07d3ByclLThIWFYWRkhJ6eHnp6epQpU4Zt27Z9/oUUQgghhBAihcjQwe/c26dvmdd4Hl5tvBh9ezR99vfh5uGb7JmyJ0Hay7sus2vyLjqu6cjYO2Op1q8ai9os4tmdZwCcWHGCg3MO0mltJ3xv+mLrZsvClgtRFIWINxFsHLyRLpu68GvArwT0CyAyNO7p3gdmHcCxkuNHg6x427Zt4+3bt4SGhhIcHIyiKPj4+CRIN336dFq3bs3OnTu5fv06ALly5eLt27fqv1y5cjF16lQOHTpESEiIBFlCCCGEECLNkEDrO5cuSzpGXhuJR1MPNBoNYc/DiI6MJl2WdAnSPr7+GBRQtAqKoqDR12BgZKA+T+To4qOUblua7I7ZMTQxpNawWry494Kbh2+ip///t4oCiqKAJq6H6uX9l5xcdZLKvSsnu+wZMmSgffv2nDp1Smf7q1evWLp0KV26dKF+/fpMmjQp+RdGCCGEEEKIVCRDB38AJunjHvo23Hk4rx6+Iq9nXko0LZEgXZEGRTix/ARjPMfEBU4a8JntQ8acGQF4dPURFXtUVNPrG+qTNV9W7l+8T4EyBWgwvgFzf5mLRk9D46mNMTIzYlnnZdQYUiPRYYqf8uLFC1asWEGDBg10ti9cuBBnZ2eKFClCt27d8Pb2ZuTIkWTNmjXZ5xBCCCGEECI1SKD1Axl0ahDhL8NZ0nEJC1stpNOaTjr7Y6NjyemSkyZ+TcjhnIPTa06zssdKrAtak8MpB5FvIzEy0w2YjEyNiAqNAsCrlRderbzUfVf3XiU2Jpa8JfOysNVCnvzzhLweeak3qh76hvqJlrFmzZoYGBig1Wp58+YNFhYWbNmyRd2vKAozZ85k5MiRAJQsWRJXV1dmzpzJsGHDUuQ6CSGEEEII8bXJ0MEfiJGpERbZLag1rBZX91wl7GWYzv6AfgFYF7QmV5FcGBgZ4NHMA7tidpxYfiLueDMjosOjdY6JCo/COJ1xgnPFRMawafgm6o+uz65JuzDLZEbfA315euspx5Ye+2AZ//rrL16+fMnr168JCwtj8ODBVKhQgTNnzgCwfft2bty4wa+//kqWLFnIkiULFy5cYMaMGURERHzpJRJCCCGEEOKbkEDrO3fr+C1GeYwiJipG3RYTFYO+kX6C3qkX914QExmjs03fUB99o7jep+yO2Xl49aG6LzY6lidBT8jumD3Beff67cW9njuWtpY8vPIQWzdbNBoNNm42PLz8MEH6xJiamtKnTx/Sp0/P7t27gbhFMDp06MCFCxc4d+4c586d49KlS0RHR7N48eKkXRQhhBBCCCFSmQRa37kchXIQFRbF5hGbiYmK4fnd52wcspGSPiUxMNIdGepczZlD8w9x9/xdtFot5zae4+bhm7jXcwfAo5kHh+Ye4v7F+0RHRLN5xGbSZ0tPvlL5dPJ5ducZF/66QIUuFQDImi8rt0/eJjY6ljtn7pAlT5YklT0mJoaFCxfy8uVLSpcuTVBQENu2bePXX3/FxsZG/ZcnTx6aN2/OpEmT4hbiEEIIIYQQIo2TOVrfOeN0xnRa04n1v69niMMQTDKYUOznYlTtUxWAfrb9+HnSzxRrVIyq/aui0dewsOVCwl6EkTVfVtosaYONiw0AHj4ehL8KZ0HzBbx99pZc7rnosLJDgvlW6waso/b/aquBXKWelfiz7Z8Myj8I+3L2lGpd6oPlrV69Ovr6+mg0GjQaDfb29qxcuZJSpUrRq1cvXF1dKVy4cILjOnbsiJ+fH5s3b6Z27dopdPWEEEIIIYT4OjSKdBF8UmBgYNz/uQPT5AOL06qs+llpmqHpVz1HWFgYV65cwdHRETMzs696LpE80jZpm7RP2iVtk7ZJ+6Rd0jZp24/UPvGxgYuLy0fTydBBIYQQQgghhEhhEmgJIYQQQgghRAqTQEsIIYQQQgghUpgshpEMdoZ2ZNLLlNrF+G5Y6Ft89XNoNBpMTU3RaDRf/VxCCCGEEEIklQRayVDK9MOr6YnUYWpqipOTU2oXI81TtFo0etKBLYQQQgjxrUiglQwx65ahPHmc2sUQIlk0Wa0wqN8stYshhBBCCPGfkqYCrZkzZ3L06FGWLFmibhs4cCDr1q3TSWdlZcXBgwc/mM+2bdvw8/Pj7t272NnZ0bdvX8qWLfvF5VOePIZH9784H/FjOvvgMb237yfw8VNMDQ1oWMie0ZXLYGyQ8Nes1tJ17L91F4N3eplW/lyTqgXyEBEdw6Ddh1h3+QZvoqJwyGKJb6XSlM+TC4Dpx87ge+AYBnp6DCnvSYfibgBEx8biNW8FSxr+hEMWSzVfeX6DEEIIIcS3l2YCrUWLFjFt2jSKFy+us/3atWt06tQJHx8fdZu+vv77h6uOHTtG3759GTBgAJ6enqxdu5YuXbqwYcMG8uXL99XKL/7btFqFuss30Ld0cXa3+pkHb95SbfFaMpuZMqhcyQTpTz94zJbm9SlrZ5tg36Ddh/j77gMOtmtCjvTmLDp7kbrLN3ChSysymZrQd8cBTnTyQVHAY85Smrk5YW5kyLRjZ6hawE4nyBJCCCGEEKkj1SdtPH78mHbt2jF16lTy5Mmjsy82NpabN2/i4uJC1qxZ1X+Wlh/+IDlv3jwqV66Mj48P+fLlo3///hQqVIg///zza1dF/Ie9iIjg4dtQtAoo/9+HpKfRYGaY8LuMWy9e8Tw8AvfsVonmFR4Tw7AKpbC1SI++nh5ti7pirK/PmYeP0f//RT8UJa6nSqPRoAHuvXrD0vOX+b2sx9eqohBCCCGESIZU79G6dOkSFhYWbNq0iRkzZnD//r9D827fvk1kZGSSe6K0Wi1nzpxhwIABOts9PDzYtWtXipZbiHdlNjOlR8ki9Nt5gP47DxCrKNRyyEePkkUTpD11/xHpjYxotuYvTj14TDZzM3p6FqVVEWcAZtaqrJN+3z93eBUZhZt1NsyMDJlWoyJ1l69HT6Nhdu3KmBkZ0mbDdkZWLI2poeE3qa8QQgghhPi4VA+0vL298fb2TnTf9evX0Wg0/Pnnnxw8eBA9PT3KlStHz549SZ8+fYL0r1+/JiwsDGtra53t2bJl4+HDh1+l/EJA3NBBE0MDpv7kTSv3Qtx8/pJGKzcxYv/fjPD20kkbGRtLSdvsjKhYGudsmdl/6y6/rN5MOmMjGhay10l7/O4Dmqz5iyHlPcmTKW65/PbFXGlfzFVNs+vmbaJjtXjlyknj1Zu5+ewlpXLlYGK18hh+ZJitEEIIIYT4elI90PqYGzduoKenR86cOZk9ezbBwcGMHTuW69ev8+eff6L33nLVERERABgZGelsNzY2JjIy8puVW/z3bLh6g/WXb3CxW2sACmXLwuDynvTati9BoOXj5oSP279L0lfOb4ePmxNrLl7TCbQWnA6k9/b9DKtQip6lEvaMAUTGxDBw1yECmtRh7KHjWJqacLKTD7WWrmPhmYvqQhkA4eHhKMq3WxojPDxc53+Rtkj7pF3SNmmbtE/aJW2Ttv1I7aMoSpKe4ZqmA61u3brRqlUrMmTIAIC9vT1Zs2bll19+ITAwEDc3N530xsbGAERFRelsj4yMxNTU9NsUWvwn3X31hqjYWJ1thnp6GCXSo7TozMUEvVeRMbGY/v98rlitlm5b9rDhyk3WNq5NxXy5P3jeiUdO0cjZgdwZM3Ap5Bl1CuZHo9FQOLsVF0Oe6qS9detWqry53b59+5ufUySdtE/aJW2Ttkn7pF3SNmnbj9I+73fsJCZNB1oajUYNsuLZ28d9OH306FGCQCtjxoyYmZkREhKisz0kJCTBcEIhUlLlfHYM3n2YMQeP07d0cYJfvmb0weM0dS2YIO2ryEgG7zlMPsuMuFllZfvNW6wMvMqW5vUB6LN9Pztu3OZoh2bkzpghwfHxbr94xYYrNzncrgkA+S0zcuzeQ1q6F+LU/UdUt9ddXCZPnjzfvEfr9u3b2NnZyRcdaZC0T9olbZO2SfukXdI2aduP1D43b95MUro0HWj17t2bly9f4u/vr24LDAwEIH/+/AnSazQaihQpwokTJ2jUqJG6/fjx4xQtmvjQKyFSglO2zGxoWpdhe48w8cgpLEyMaOLqyJByngBk8vVjRq1KNHV1pHvJIoRGRfPzyk2EhIaRJ5MFC+pVo3RuG56GhjPr5Hn0NRoKz9BdKTP++Hi/bdvH2CplMTKI6zXrV6YEzdZsIfu4WXjnzUWHd+ZxAan2pmZqaoqZmVmqnFt8mrRP2iVtk7ZJ+6Rd0jZp24/QPkkZNghpPNCqWbMmv/76K7NmzaJGjRrcunWL//3vf9SsWVNdifDNmzdER0erS763bt2aDh064OTkRNmyZQkICODKlSv4+vqmZlXEf0DFfLk/OMzvxaBu6s8ajYbfy5Xk90Ser5XF3JSIYb8l6Xzrm9bVeZ09fTr2tvkl6QUWQgghhBBfTao/R+tjKlSowNSpU9m5cye1atVi0KBBVKlShVGjRqlpfH19adiwofq6dOnSjBo1ihUrVlCvXj2OHTvG7Nmz5WHFQgghhBBCiG8mTfVojRkzJsG2qlWrUrVq1WQdU7duXerWrZuSRRNCCCGEEEKIJEvTPVpCCCGEEEII8T1KUz1aaZ2mQEGULNlSuxhCJIsmk2XqnFejwdTUNMkTRoUQQgghfiQSaCWDgfdPqV0EIb4bpqamODk5fTrhO2IVBX0JzIQQQgjxA5BAKxl8nwYTHB2Z2sUQ4oeU29CYQVk+/HBmIYQQQojviQRayRAcHcmNqPDULoYQny3q2XPO1G+Jw5ihZPIsDsDrs4HcGD6W0BtBGFlmIne39mT/pV6ixytaLYecvUBR4J2eJ69Te3lz8QoXWnXRTR8TgxIVjefxnRhlyczVvsN4unMfRtmy4jB2GBmLuwMQfuceAZ160+fMeYyNjdXjX7x4waBBg9i8eTPPnz8nQ4YMVKlSBV9fX2xsbAB49uwZvr6+bNq0iUePHpEhQwYqVKjAwIEDcXZ2VvOKjIxk+PDhrF69msePH2NqakrZsmX5448/cHR01Cn3s2fPsLW1xd7ennPnzn3+BRdCCCHEf5YshiHEf8SrU2c5U78lEcF31W3Rr15zoXVXrBvUpPSFQziMG87NkRN4fS4w0TxCb/yDEhND6fOHKHv5qPpP38yUjCWK6GwrdWI3prltsevdBWOrbDw/+DevTp6l5JFt5Gz+M0G+k9R8bwwfi8eQ/jpBFsAvv/zC06dPOXnyJKGhoZw7d47IyEgqV65MTEwMDx48wM3NjTt37rB582Zev37NuXPnyJs3Lx4eHuzatUvNq1u3bvz999/s2bOHt2/fcuPGDWxtbSlbtiwvX77UOe/8+fOpXr06Dx8+1MlDCCGEECKpJNAS4j/g0dpNXO7xO3n7dNXZ/mTbbgwzWZCzRWP0DAzIVKoEVnV+4v7iVYnm8+b8RdIVLICekeEnz3lj2BiMrbNh1609ABp9fZ39Gv24t58nO/aib2qKTTmvBHkcPnyYevXqYW1tDYCVlRVTpkzBw8ODFy9e0KtXL3Lnzs3q1atxdHRET0+PbNmyMXLkSLp3706rVq2IiYlR86pWrRp2dnYAZMyYkfHjx1OrVi0ePXqknlOr1TJr1iyaNWtGu3btmDhx4ifrKoQQQgjxPhk6KMR/QKaypchW9yf0DAy43K2/uj3sehDmDgV00poVyMvDVesTzefNhUvERkRyunZTIu49xCx/HvL2745F0cI66V6eOMOTv3ZSYs+/+WQqXZJMpUtyokIdjKyy4jBmKLHh4dyeNBPXxTMTPV+TJk3o1KkThw4donz58nh4eJA7d24WLVpETEwM69evZ9q0aejpJfzOqH379owZM4a///6bsmXL0qRJE0aMGMG1a9eoWLEiHh4e2Nvbs2DBAp3jNm3aRGxsLLVr16ZEiRLkzZuXwMBAXFxcPnqNhRBCCCHeJT1aQvwHGGfLgp5Bwu9VYkLD0Dcz1dmmb2pCbFjicxH1TEzIUNgF57mTKfn3NjJXKseFFp0Jv3tfJ93tKbPJ4dMIE5sc6jaNnh4Oo4fgdWYfxbetJoObM8F+87BuVIfo5y/ZXK8ZRYsWZfbs2eox8+bNY8aMGdy5c4cOHTpgZ2dH/vz5WbZsGU+fPiUqKopcuXIlWtb4OVz378eVbciQIaxZs4bQ0FB69+6Ng4MDOXPmZPLkyTrHTZ8+na5du2JgYICNjQ3169dn0qRJCfIXQgghhPgY6dES4j9M39SEyNdvdLbFhkdgYG6WaPr8g3vrvM7VoSWP1mzk2d5D2LRsDEB48F1eHjuFw9hhHz13WNBtXhw+hvu6xZyt34JiHduyvllb7O3tKVu2LE5OTujp6eHj44OPjw+KonDlyhWWLFlC8+bN2bZtG4aGhgQHByea/4MHD4C44YbxatWqRa1atQAICgpi3bp1DBgwgAwZMtC2bVuuXLnCnj17OHXqFOPHjwcgIiKC6OhoRo0aRfbs2T9aJyGEEEKIeNKjJcR/mLlDfsKuB+lsC7vxD+YO+RNN/894P95cvKqzTRsVjb7Jv4tYPNm2G4tihTG1zfnRc98YPob8Q/uiZ2BA6LUgsrgWwsLCgnz58nHp0iV27NhBunTpeP78ORD3AGQnJydGjx6Nu7s7gYGB1KtXjwULFqDVagF4/vw5M2bMIDQ0FH9/f6ysrPDy8uLKlSuYmJhw8eJF9fz58uWjb9++1KxZk7NnzwJxvVk//fQTFy9e5Ny5c5w7d46rV6+SN29e/Pz8knhVhRBCCCEk0BLiPy1rtYpEPXnKXf+laKOjefH3SR5v3Ip1o7qJpg+9FsTN/40jMuQp2sgobk+dQ+zbt2Sp6q2meXXqHBYlinz0vCGbd2Cc3RqLYnHLu5vmyUXIqbM8e/aM69evkz9/fsqWLYuVlRWtW7cmMDCQ6Oho3rx5w7Jly7hx4wY1atRgypQpPHr0iHr16nH58mXevn3LunXrsLe3Z+zYsSxYsABjY2MKFixI0aJF6dixIydOnCAiIoKwsDC2bdvGvn37qFevHq9fv2bx4sW0b98eGxsbnX/t27dn9uzZhIaGpti1F0IIIcSPTQItIf7DDDNlxHXpbJ5s3cUR9/JcGzCC/MP6kalU3DO2Xp44w0EnTyLuPwSg4IQRmOay4dRPP3PYvRwvj53CbekcDDNaqHmG37mHsVW2D54z5m0owTPmk3dAD3Vbgf8N5Oy02Tg4ONCtWzfc3d0xNTXl8OHDWFtbU6tWLSwsLLC1tWXp0qXs2rULR0dHsmfPztmzZ8mbNy81a9bE0dGRS5cuUbp0aTw8PJg4cSJnzpxBo9GwdetWSpUqhY+PD5kzZ8ba2prRo0ezbNkyKlasyKJFizA1NaVGjRoJytyyZUvCwsLw9/dPoSsvhBBCiB+dRlEUJbULkdYFBsY9U8gvi7E8sFiIr6SAkSlzs9unWH6KorBlyxby5MlDoUKFUizfH0VYWBhXrlzB0dERM7PE5+SJ1CFtk7ZJ+6Rd0jZp24/UPvGxwadWJJbFMIQQPySNRkPNmjVTuxhCCCGE+I+SoYNCCCGEEEIIkcKkRysZchsafzqREOKzyO+XEEIIIX4kEmglw6AsuVO7CEL80GIVBX2NJrWLIYQQQgjxxWToYBJFRUURHi4LYaQ14eHhXL58WdomDfqctpEgSwghhBA/Cgm0kkEWaEx7FEUhPDxc2iYNkrYRQgghxH+ZBFpCCCGEEEIIkcIk0BJCCCGEEEKIFCaBlhBCCCGEEEKkMAm0hBBCCCGEECKFSaAlhBBCCCGEEClMAi0hhBBCCCGESGESaAkhhBBCCCFECpNASwghhBBCCCFSmARaQgghhBBCCJHCJNASQgghhBBCiBQmgZYQQgghhBBCpDAJtIQQQgghhBAihUmglQwajSa1iyDeo9FoMDU1lbYRQgghhBBpikFqF+B7YWRkhKmpaWoXQ7zH1NQUJyen1C7GJ2kVLXoa+V5DCCGEEOK/QgKtZNgeup3nsc9TuxjiO2Opb0k182qpXQwhhBBCCPENSaCVDM9jn/Mk9klqF0N8QOiLUNb/vp7LOy+jaBXyeeWj0YRGWFhbJEh7fPlx9kzdw6uHr7AuaE3t4bXJVyofAFHhUWwYtIHArYHERMZg42ZDPd965CiUA4ADcw6wc/xO9Az0qNa/Gl6tvQCIjY5lcuXJNJ/XHKsCVh8sp52dHY8ePcLAwACNRoNWq8XCwoJmzZoxduxY9PT0WLhwIX5+fty4cQM9PT1cXV0ZMGAANWrUUPNRFIV58+Yxb948rly5gqGhIU5OTnTs2JEWLVqk5KUVQgghhBDJJGOZxA9jYcuFRIVGMfjMYIZdGIaenh6req5KkO7itous6b2GOv+rw+hbo/Hu5s2cn+fw+MZjALaP3c6ToCcMODqAkddGkqNQDvyb+wMQ8SaCjYM30mVTF34N+JWAfgFEhkYCcGDWARwrOX40yIo3e/Zs3r59y5s3bwgNDWXHjh38+eefjBgxguXLlzNw4EBmzpzJq1evePLkCe3bt6d+/focPHhQzaNZs2aMHj2aQYMG8fjxY0JCQujfvz+DBg2ibdu2KXFJhRBCCCHEZ5IeLfFDuHvuLsGnghl5dSQmGUwA+GXqL7x+9DpB2tNrT1OkQREKVS0EgFstN44uPsrxZcepPbw2j68/RqvVggIooKevh5GpERD3M8RtVxQFNHELcry8/5KTq07y2+7fPqv8Li4ulC1bljNnzvDkyRPc3NwoWbIkEDc/sEWLFgQHB/PixQsANm7cSEBAAFeuXCFv3rxqPrVr1yZ//vy4urrSsGFDqlev/lnlEUIIIYQQX0YCLfFDCD4TjJWDFUcXH+XIwiNEhUVR0Lsgdf6okyCtNlaLsbmxzjY9PT1CboQAUKFLBRa2XMig/IPQ09fDPLM5XTZ2AcDIzIgG4xsw95e5aPQ0NJ7aGCMzI5Z1XkaNITXUgCw5oqOjOXLkCHv37mXEiBEUKlSIqlWrUq1aNWrWrEnJkiVxc3NjyJAh6jHr16/Hy8tLJ8iK5+TkRKlSpVizZo0EWkIIIYQQqUSGDoofQtiLMB5cesCTf57QZ38f+h7oy6uHr1j267IEad1quXFy5UluHrlJbEwsgVsDuX7wOtHh0QBoY7S41nJl+MXhjPpnFC4/ueDv4090RNx+r1ZeDL84nGEXhlGiSQmu7r1KbEwseUvmZWGrhYwrO461fdcSGx37wfJ27tyZjBkzkjFjRrJmzUrXrl3p3bs3Xbt2xdvbm9OnT2NnZ8eUKVMoXrw4lpaW/Prrr7x9+xaABw8eYG1t/cH8c+TIwYMHD77kkgohhBBCiC8ggZb4IRgYxXXO1htVD5P0JqTPlp4ag2twZdcVIt9G6qQt0qAIPw36iVU9VzHUcSgX/rpAkfpFMM1oSmx0LItaL8KjqQcZc2TEJL0JDcY24NXDV1zbfy3BeWMiY9g0fBP1R9dn16RdmGUyo++Bvjy99ZRjS499sLwzZ87k5cuX6r+LFy8yaNAg9Xlgrq6uzJ49m5s3bxISEsLcuXP566+/+PXXXwHInj07wcHBH8z/1q1bZM+ePdnXUQghhBBCpAwJtMQPwbqgNYpWITbq314kbawW+P+5VO94/fg1jhUdGXRyEL43fGk2sxmPrz8mV+FcRIZGEvYyjJioGDW9Rl+DRk+DvqF+gvPu9duLez13LG0teXjlIbZutmg0GmzcbHh4+eFn1SVXrlzMmDFDfZ01a1aaNGlCv379OHv2LACNGjXi5MmTBAYGJjj+7NmznDlzhgYNGnzW+YUQQgghxJeTQEv8EBzKO5DZLjMruq0g8m0kb5++ZYvvFlxquGCS3kQn7c0jN5leezrP7z4nOiKa/bP2E3IzhOJNimOW0Yy8JfOyefhm3jx5Q3RENJuHb8bc0py8JXXnQz2784wLf12gQpcKAGTNl5XbJ28TGx3LnTN3yJIny2fVxcfHhz/++IO//vqLV69eERMTw/nz5/H391eDp5o1a9KsWTNq1arFpk2bCA0NJTQ0lI0bN1KnTh2aNGlCzZo1P+v8QgghhBDiy8liGOKHoG+oT7fN3dgweAO+xX2JjojGuboz9UbXA6CfbT9+nvQzxRoVo0j9IoTcCGFK1SlEvo3Exs2GLhu7kD5regBaL2rNxmEbGVdmHLHRsdgVs6PT2k4JFtBYN2Adtf9XWx22WKlnJf5s+yeD8g/Cvpw9pVqX+qy6+Pr6kiNHDoYPH861a9fQarXkyZOHdu3a0aNHDzXdwoULWbBgAaNHj1afm+Xk5MSIESNo1arVZ51bCCGEEEKkDI3y/rgqkUD88KzA3IHywGKRbFn1s9I0Q9PULsY3FxYWxpUrV3B0dMTMzCy1iyPeI+2TdknbpG3SPmmXtE3a9iO1T3xs4OLi8tF0MnRQCCGEEEIIIVKYBFpCCCGEEEIIkcJkjlYy2BnakUkvU2oXQ3xnLPQtUrsIqUKj0WBqaqouWS/SFmkfIYQQ4uuSQCsZSpl+3uIGQvwXmZqa4uTklNrFEB+Q1ttH0WrR6MmgCyGEEN8vCbSSIWbdMpQnj1O7GEII8UPTZLXCoH6z1C6GEEII8UUk0EoG5cljeHQ/tYshhBBf7EloGGXnr2B27SqUy2MLwIVHT+i7Yz8n7z/CzNCQJi4FGV25LAb6CXuWImNiGL7vb1ZeuEpodDRl7WyYXN0bW4v0OunCoqKpungt7Yu60sK9EACxWi3tNuxg09Ugsqc3Z07tKnjlzgnAP89f0ihgAidrNMTY2DjBeYUQQojvRaqPy3j58iVDhw6lbNmyFClShCZNmnDq1Cl1/5UrV/Dx8aFw4cKUL18ef3//T+a5bds2fvrpJ1xcXKhVqxYHDx78mlUQQojvyt937lN2/gqCXrxStz0NDafa4rV4583N4/6dOdyuCVuu/8O0Y2cSzWPw7sNsuHyDv5rX516fTuS3zET1xWuJiolV01wKeYr3wtUcv/dQ59idN2/z950HBP3Wjk7F3ei/84C6r9e2fYxtUEuCLCGEEN+9VA+0evXqxfnz55k0aRJr166lUKFCtG3blqCgIF68eEHr1q2xs7MjICCAbt26MXXqVAICAj6Y37Fjx+jbty9NmzZlw4YNlC5dmi5duhAUFPQNayWEEGnT4nOXaBGwlREVS+tsX3L+EgUyZ6J/mRIY6utjl8mCbS0a0rCQfaL5rAy8yu/lPCmULQtGBvr4VirN/ddv2XvrDgD7/rlD1T/X0rywE7ne6+Uy+P+5V/EPcdT//9cbr9zEzMiQSk4OKVhjIYQQInWk6tDB4OBgjhw5wooVKyhSpAgAgwYN4uDBg/z111+YmJhgZGTE8OHDMTAwIF++fAQHBzNv3jwaNGiQaJ7z5s2jcuXK+Pj4ANC/f3/Onj3Ln3/+yf/+979vVjchhEiLquSzo6mLIwb6evis3aJuP3n/EYWyZabL5t1sunoTcyNDWro70790iUTziVUUzI3+/ROiQYNGA9eePqdagTy4WmflZs92mBgaMOXv0zrHVsybm4r5clHIbyE50qdjdu3KhEVFM3zfEbb4JP7eLoQQQnxvUrVHK1OmTMydOxdnZ2d1m0ajQVEUXr16xalTpyhevDgGBv/+MS9ZsiS3bt3i2bNnCfLTarWcOXOGkiVL6mz38PDQGY4ohBD/VdbpzROdc/UiPII/z16ieE5r/unVnlW/1Gb+qQtMOXo6kVygnmMBxhw8QdDzl0RExzBs3xHCo2MIj44BILOZKSaGiX+Xp6enYWatyjzo9yunfm1OsZzWjDp4nFbuzjwNC8d7wgyKFi3K7NmzU67iQgghxDeWqj1aGTJkoFy5cjrbtm3bxp07dyhdujSTJ0/G3l532Eq2bNkAePDgAZkzZ9bZ9/r1a8LCwrC2tk5wzMOHunMEhBBC/MtYX5/iOa1pVSTuiy8366x09ijM2kvX6OVVLEH6cVXL8fuug1RcuAoDPT1aF3HB2SoLmUxNkn3ua0+fs+efYA61bUIZ/xX0qF6J2hNnYG9vT/HixXF0dPzi+n1PwsPDdf4XaYu0T9olbZO2/UjtoyhKkp5DmaZWHTx9+jS///47FStWxNvbm9GjR2NkZKSTJn6CdGRkZILjIyIiABI9JrH0Qggh4hTMmpkDt+/qbIvVKuo8qvfdf/2WgWVLMrVGRSCuR2zsoeMUzWGV7HP/tnUfE6uVx0Bfj0shTymSyxYLCwty5MjB7t27k53fj+L27dupXQTxEdI+aZe0Tdr2o7TP+/FGYtJMoLV792769OmDm5sbkyZNAsDExISoqCiddPEBk5mZWYI84oOwxI4xNTX9GsUWQogfQit3Z2YcP8uEwyf5rVRRLj95xqwT5+idSG8WwLRjp7n94hUrf65FtFZLty17KJLdimI5rRNN/yGrL17DxiIdpXLFLe+e3zITx4JuYf3sGXfv3qVMmTL/yR6t27dvY2dnJ3+70iBpn7RL2iZt+5Ha5+bNm0lKlyYCraVLl+Lr60vlypWZMGGCGiFaW1sTEhKikzb+tZVVwm9NM2bMiJmZWaLHvD+cUAghxL8KZrVkT+ufGbDzIOMOn8DM0IAOxdzo4uEOwOHge9Raup7zXVqSK2MGRlUqQ5e/dpN/ynwAquS3I6BJnWSd801kFGMOHmdny0bqtmk1vOnw1x76b3GgW7dulCpVKuUq+Z0xNTVN9EtFkTZI+6Rd0jZp24/QPkkZNghpINBavnw5I0eOpHnz5vz+++/o6f07Sbt48eKsXLmS2NhY9PX1ATh69Ch58uRJMD8L4ipdpEgRTpw4QaNG//7hPn78OEWLFv36lRFCiO9I1PBeOq9L2GRnb5tfEk1bOrcNLwZ1U19nMDFmScMaSTrPjd/aJbo9vbERZzq3SHCeyyMHYtixV6LHCCGEEN+LVF118NatW4waNYrKlSvTsWNHnj17xpMnT3jy5Alv3ryhQYMGvH37lkGDBnHz5k3WrVvHn3/+SceOHdU8/o+9O4+P6fr/OP6aLBOTxBJrQhASQdROxRZK1U5VUUTxs2+t2lUVX7VV7aX5VkO11YXaqVpbS1FrNIh9K0FIYsu+zO+P1HybJjTR6ATv5+MxD51zzz33c+f0Mp+5555z7949IiIiLO+7d+/Ohg0bWLx4MefOnePDDz8kJCSErl27WuMURURERETkOZQld7Ru3rxJWFgYZcqUsdx5yohNmzaRkJDAli1b2LJlS6ptbdq0YerUqXz22WdMmjSJNm3aUKBAAUaMGEGbNm0s9SZNmsT+/fvZvn07AHXq1GHy5MksWLCAWbNm4eXlRUBAAJ6enllxqiIiIiIiIn8r04lWVFQUH3zwAT4+PnTp0oUffviBESNGkJSUhIeHB4sWLcLNzS1DbfXt25e+ffs+sk6FChX47rvvHrp96tSpacpeffVVXn311QzFICIiIiIiktUynWh99NFHbNq0idq1awMwY8YMypQpQ79+/Zg9ezYfffQRM2bMyPJAswNDqTKY8xe0dhgiIs80g0tea4dgVQaDAZPJlOGHrUVEJHvKdKK1bds2Ro0aRYsWLQgJCeHq1auMGDGChg0bkpiYyLhx455EnNmCXYNm1g5BRESecSaTCR8fH2uHkSFJZjO2SghFRNKV6UTr9u3blCxZEoCff/4ZOzs7y92t3LlzP9MLA0+6dYlLCc/u+YmIiGRUcXsHxuQvbu0wRESyrUwnWkWKFOHUqVNUq1aNzZs3U6lSJZydnQHYsWMH7u7uWR5kdnEpIY4z8THWDkNERJ5h90+c4tzkmdwLDsFgb09ev5p4vjcUY14Xbm7cysW5nxL7+1XscufCrV1rir/VG4NN+pMIXw5YzJXPvyHxzl1yVihH6cljcfT0SFUnKSaGo5364NapLW7tUtZCMyclcXL4OG5t/gljwQKUnjaOPNVT1lSLuXyFY72H0GHjCsif+niRkZGMGTOGdevWERERQa5cuXjllVeYNGmS5ftBeHg4kyZNYu3atVy/fp1cuXLx0ksvMXr0aF544QVLW3FxcYwfP55ly5Zx48YNTCYTfn5+fPDBB2kWsQ4PD6do0aJ4e3sTFBT0Dz59EZGsk+np3Tt16sTUqVNp2rQpISEhdOrUCYBBgwbx+eef88Ybb2R5kCIiIs+DpNhYfus2kFxVKlLrwDZe3LKChMjbnBo+jnvBJwh55z1KDBtInd92UWHJfK5/v5YrgV+l29b179dy5fNvqPjFAmof2UHO8j4c6zcUs9lsqRN1+ixH2vfg7pHfUu0bsXMPdw4cwfeXjRTp0p5zk2Zatp0ZPw3PMe9g62BMc8wOHTpw69YtDhw4QFRUFEFBQcTFxdGoUSMSExMJDQ2lYsWKXL58mXXr1nH37l2CgoIoWbIkNWrUSDUD8aBBg9izZw/btm3j/v37nDlzhqJFi+Ln58ft27dTHfezzz6jadOmXLt2Lc0sxiIi1pLpRKtLly5MnTqVF198kZkzZ9KsWcpzS3Z2dowfP57OnTtneZAiIiLPg7ir13Eu643H232wMdpj75KHwp1e5/b+w8ReCaVw59fJ39APg40NTl4lyd+4Abd/PZxuW6HfrqRIl/Y4eXthm8OBkiPfIi70Orf3HgQgcs9+gjr1xrVtSxyKpJ4t2PCXpVoMtilfF25u2o6tyUTeujXTPebu3btp06YNrq6uABQqVIjZs2dTo0YNIiMjGTJkCMWLF2fZsmWULVsWGxsbChYsyMSJE3nrrbfo1q0biYmJlraaNGmCh4cHAHny5GH69Om0bNmS69evW46ZnJzMJ598QufOnenZs+czOyGXiDx9HmsdrebNm9O8efNUZbNmzcqSgERERJ5Xjp4eVFgyP1XZzY1byflCWQo0fZkCTV+2lCfFxhL+0y4KtU5/oqbo0+co1re75b2NvT0mj2LcDzmFS63qOJf1xnf3RmxzOPD7wi9S7etSxxeXOr7sf6k1xkIFKD31fZJiYrg4cwEVvljw0Pg7duxI37592bVrF/Xr16dGjRoUL16czz//nMTERFatWsXcuXOxSWeoY69evZg6dSp79uzBz8+Pjh07MmHCBE6dOkXDhg2pUaMG3t7eLFq0KNV+a9euJSkpiVatWvHiiy9SsmRJgoODKV++/MM/aBGRf0GGEq3Vq1dnqlGtYSUiIvLPmM1mLsyYT/jWHVRaljq5SLwfxfF+w7BxcMC9h3+6+ydGRWPraEpVZmvKQVJ0yrPG9i55Hnpsg40NpaeMpfSUsZay8x/OxbVdaxIibnNi0CiOxcRRpf/AVOthLly4kJdeeolvv/2W3r17c+fOHTw9PZkwYQINGzYkPj6eYsWKpXvMB89wXb16FYCxY8dSqVIlvvjiC4YOHcrNmzcpXLgww4YN45133rHs9/HHHzNw4EDs7Oxwd3fntddeY+bMmSxevPih5yci8m/IUKI1atQoy3oefx7bnR6DwaBES0RE5B9IvHefk388l1Vp2SKcy5SybIs+d5Fj/YZizJ+PSt9+hp2zU7pt2DqaSIqJTVWWFBOLnZNjpuOJPneRyN37qLzyC4689ibuPbrwYuNGjPNrhp+fn2U6ehsbG/z9/fH398dsNhMSEsKXX35Jly5d2LhxI/b29ly6dCndY4SGhgIpww0faNmyJS1btgTg3LlzrFy5klGjRpErVy569OhBSEgI27Zt4+DBg0yfPh2A2NhYEhISmDx5Mm5ubmkPJCLyL8nQM1oFChTAbDZTtmxZhg8fzoYNG9i2bVu6r61btz7pmEVERJ5ZMZd+51DrziTdv0/VdV+nSrLCf9rFoVf9yVuvNhW+WIB97lwPbcfJ25Po0+cs75MTEoi5eBmn0l6ZjunM+Kl4vT8cGzs7ok6dI2d5H4y5cuLp6cnx48cB2LRpE87OzkRERAApP7z6+PgwZcoUKleuTHBwMG3atGHRokUkJycDEBERwfz584mKiiIwMJBChQpRu3ZtQkJCyJEjB8eOHbPE4OnpyfDhw2nRogVHjhwBUu5mNWvWjGPHjhEUFERQUBAnT56kZMmSzJs3L9PnKSKSlTKUaO3cuZMvvviC8uXL89lnn9G+fXvmzJnDuXPncHV1pUiRIqleIiIiknkJd+4S1Kk3uatUpMIXn2DM62LZdufwbxzrMwSvscPwGjMEG7tHD0pxa/8qV5Z8w/0Tp0iKjeP8tLkY8+cl94tVMhVT2LpNOLi5krtayvTuphLFuHMoiNiISE6fPo2XV0ri5ufnR6FChejevTvBwcEkJCRw7949li5dypkzZ2jevDmzZ8/m+vXrtGnThhMnTnD//n1WrlyJt7c306ZNY9GiRTg4OFCmTBmqVq1Knz592L9/P7GxsURHR7Nx40Z++ukn2rRpw927d/niiy/o1asX7u7uqV69evUiICCAqKioTPaAiEjWyVCiZTAYePHFF5kwYQK7d+9m9uzZ2NraMmzYMGrXrs3777/P/v37n3SsIiIiz7Try9cQd/UaYRs2s+uF2uz0qWl5XV4QiDkhkTPjp6Uq/63rAABu7z/MTp+axF69BoBr+1cp2sOfY32GsKfqS9w/fpLyiz7Gxt4+w/Ek3o/i0vzPKDnqbUtZqf+M5vL8z1hWtwmDBg2icuU/EjCTid27d+Pq6krLli3JnTs3RYsW5auvvmLLli2ULVsWNzc3jhw5QsmSJWnRogVly5bl+PHj1KlThxo1ajBjxgwOHz6MwWDghx9+oFatWvj7+5MvXz5cXV2ZMmUKS5cupWHDhnz++eeYTKY0k3MBdO3alejoaAIDA/9Jd4iI/CMG8989dPUICQkJ7Ny5k40bN7J9+3acnZ1p1qwZo0aNysoYrS44OBiAefkdtGCxiIgIUMpo4lM37yxrz2w2s2HDBkqUKEG5cuWyrN2sFh0dTUhICGXLlsXRMfPPu8mTo77J3p6l/nmQG/zd7KaZXkfrz+zt7WnYsCHdu3enXbt2REREsGTJkn/SpIiIiDyHDAYDLVq0yNZJlohIZjzWOloAJ06cYOPGjfz4449cuXKFwoUL07VrV8sCxiIiIiIiIs+rTCVaISEhluTq8uXLFCpUiCZNmtCsWTMqVqz4pGLMNorbO1g7BBERkWxB/yaKiDxahhKtWbNmWZKrfPny0bhxY5o2bUq1atWedHzZypj8xa0dgoiISLaRZDZj+8c6myIiklqGEq3//ve/2NraUq1aNV588UUMBgP79u1j3759aeoaDAYGDBiQ5YFaW3x8PDExMZhMJmuHIn8SExPDhQsXKFGihPomm1HfZG/qn+zraeobJVkiIg+X4aGDSUlJHDhwgAMHDjyy3rOaaEHKjEiSvZjNZmJiYtQ32ZD6JntT/2Rf6hsRkWdDhhKtkydPPuk4REREREREnhkZmt7922+/1erqIiIiIiIiGZShRGv8+PHUqVOH0aNHc+jQoScdk4iIiIiIyFMtQ4nW6tWrad++Pbt27cLf358mTZoQGBhIeHj4k45PRERERETkqZOhRKtMmTKMHj2aHTt2sGDBAkqXLs2cOXOoV68eAwcOZMeOHXpoV0RERERE5A+ZWrDY1taWl156iZdeeol79+6xfv161qxZQ58+fShUqBBt2rTh9ddfx93d/UnFKyIiIiIiku1l6I5WenLmzEnHjh359ttv2bx5M506deKHH36gcePGWRmfiIiIiIjIU+exE60HwsPD+fnnn9m5cydXrlyhSJEiWRGXiIiIiIjIUytTQwcfiI6OZsuWLaxdu5Zff/0VOzs7XnnlFd5++21efPHFrI5RRERERETkqZLhRCspKYndu3ezdu1atm/fTkxMDC+88ALvvfceLVq0wNnZ+UnGKSIiIiIi8tTIUKI1ceJENm7cSGRkJLlz56Zdu3a8/vrreHt7P+n4REREREREnjoZSrS++eYbatWqRdu2bXn55Zext7d/0nGJiIiIiIg8tTKUaG3fvh1XV9cnHUu2ZzAYrB2C/IXBYMBkMqlvRERERCRbyVCipSQLjEYjJpPJ2mHIX5hMJnx8fKwaQ7I5GRvDP57AU0RERESeIY816+Dz6seoH4lIirB2GJKN5LXNSxOnJtYOQ0RERESyGSVamRCRFMHNpJvWDuO5FBUZxap3V3Fi8wnMyWY8a3vS7qN25HbNnaZuQLsAzu4+i43d/+4ydV/cnbIvl01Vb+8Xe/lu8HfMjphtKdvx3x1snr4ZGzsbmoxsQu3utQFISkhiVqNZdFnYhUKlCj00Tg8PD65fv46dnR0Gg4Hk5GRy585N586dmTZtGjY2NixevJh58+Zx5swZbGxsqFChAqNGjaJ58+aWdsxmMwsXLmThwoWEhIRgb2+Pj48Pffr04c0333zcj1FERERE/iUZSrTWrFmDn58fLi4uTzoekXQt7roYxzyOvHf4PWxsbPh6wNd8N/g7en/bO03d34N+p+/3ffGq7fXQ9q6FXGP1e6tTlcXei2XNe2sYtmMYmOGj+h9RrX01HJwc2PHJDsq+XPaRSdYDAQEBdOvWzfI+ODiYhg0b4ujoSOnSpRk9ejSrV6/mxRdfJDExkW+//ZbXXnuNLVu24OfnB0Dnzp3Zu3cvs2bNolGjRhiNRjZu3MiAAQPYsWMHgYGBGfvgRERERMQqMpRojR8/nsDAQFxcXGjYsCHz58+nTJkyTzo2ESAlcbp08BITT04kR64cAHSY04G71++mqRt+KZzoyGjcK7g/tL346Hi+6PkFfn382DJji6XcxvaPO2DmlDtKGFIm27h99TYHvjvAO1vfeaz4y5cvj5+fH4cPH+bmzZtUrFgRX19fIOXZvzfffJNLly4RGRkJpPywsWLFCkJCQihZsqSlnVatWuHl5UWFChV4/fXXadq06WPFIyIiIiJPXoYSLaPRyJo1a0hMTOTq1asEBQVx7969h9avXr16lgUocunwJQqVLsTeL/byy+JfiI+Op0yDMrT+oHWaupcPX8bB2YElPZZw+chlchbISf3+9fH197XU+X749/g09sG7nneqRMvoaKTt9LZ82uFTDDYG3pjzBkZHI0v7L6X52OYYTcZMx56QkMAvv/zC9u3bmTBhAuXKlaNx48Y0adKEFi1a4OvrS8WKFRk7dqxln1WrVlG7du1USdYDPj4+1KpVi+XLlyvREhEREcnGMpRotWvXjs8++4xly5ZhMBiYMGFCuvXMZjMGg4GQkJAsDVKeb9GR0YQeD6VY5WIM+3kYCTEJfNX3K5b2W5pm6GBifCIe1T1o/l5z3Mq6cWbXGRZ3W0wO5xxUerUSB5cd5MbpG3SY04Hz+86nOVbtbrWp3a225f3J7SdJSkyipG9JFndbzM3zNylZoyRtJrfB1t423Xj79+/P4MGDLe/d3d0ZOnQoAwcOxGAwcOjQIRYsWMDs2bM5d+4czs7O+Pv7M336dJydnQkNDX3kTJ+FCxcmNDQ0k5+iiIiIiPybMpRoDRs2jNatWxMZGcmbb77J+++/j5fXw59/EclKdsaU/03bTG6DfQ57cuTMQfP3mjOr0Szi7sfh4OxgqVu9Q3Wqd/jfHdUyDcpQvUN1jqw6gls5N9ZNWMdbP7yFrV36SdKfJcYlsnb8Wnou7cmWmVtwdHFk+I7h/Lfdf9n31T7LRBl/tWDBglTPaP1VhQoVCAgIAODmzZts3bqVESNGcP/+fb788kvc3Nw4e/bsQ/e/cOGC1ae0FxEREZFHy/Csg6VKlQJg4MCBNGzYkEKF/n5SAJGs4FrGFXOymaT4JOxz2AOQnJQM/PEs1Z/s+2qf5e7VA4lxidib7Dm69igxd2KYXm96qjZGeYyi3UftqPp61VRtbZ+3ncptKpO3aF6uhVyjQvMKGAwG3Cu6c+3Etcc6l2LFijFy5EgGDBgAQIECBejYsSO3bt3iv//9L5ByB/m1114jODiY8uXLp9r/yJEjHD58ONVQQxERERHJfjI9vfvAgQOJj4/n22+/5ddff+Xu3bu4uLhQrVo12rRpg4ODw983IpIJpeuXJp9HPr4Z9A2d5nciITaBDZM2UL55eXLkzJGqbuzdWNZPXE/+kvkp/EJhQraGcGjFIfp+3xfPmp68MvQVS90zu88wv9V8pl6cmuaY4ZfD+W39bwzePBiAAp4FuHjgIjU61+Dy4cv4NHq8O0r+/v588MEHFC9enLp16+Lk5MTx48cJDAykbdu2ALRo0YLOnTvTsmVL5s6dS8OGDQHYunUrgwYNomPHjrRo0eKxji8iIiIi/45MJ1p3797lzTff5OTJkxQuXJgCBQpw4cIF1q9fz9KlS/n666/JmTPnk4hVnlO29rYMWjeI1e+tZlL1SSTEJvBC0xdoM6UNACOKjqD9zPZUa1eNev3qER8dT2CXQO7fuk++4vnovKAznjU9M3XMlaNW0uo/rSzDFl8e/DJLeixhjNcYvOt5U6t7rcc6l0mTJlG4cGHGjx/PqVOnSE5OpkSJEvTs2ZO3337bUm/x4sUsWrSIKVOmWNbN8vHxYcKECY8cligiIiIi2YPB/NexV39j3LhxbNq0iY8//phq1apZyg8ePMhbb71Fs2bNeO+997I8UGsKDg5O+bN4sBYsllQK2BagU65O1g4jW4qOjiYkJISyZcvi6Oho7XDkL9Q/2Zf6JntT/2Rf6pvs7Vnqnwe5wV8f8fgrm8w2vG3bNgYPHpwqyQKoVq0ab731Fps3b85skyIiIiIiIs+UTCdaUVFRFC1aNN1tRYsW5fbt2/80JhERERERkadapp/RKlmyJD/99BO1a6ed2nrbtm0UL148SwLLjjzsPXCxcbF2GJKN5LbNbe0Qsi2DwYDJZMJgMFg7FEmH+if7Ut+IiDwbMp1o9ejRgyFDhhAfH0/Lli3Jnz8/t27dYt26dSxfvpzx48c/gTCzh1qmx5sAQeR5ZDKZtN5XNqb+yb6yW9+Yk5Mx2GR6AIyIyHMv04lWs2bNuHjxIgEBASxfvhxIWcvIaDQyYMAAOnTokOVBZheJK5divnnD2mGIiIj8KwwFCmH3WmdrhyEi8lTKdKIF0L9/f/z9/QkKCuLOnTvkzp2bihUrkjv3sz2MynzzBly/au0wRETkKXf0+k1Gbd7B4dAbGG1tedmzONMb1ye/k8lSZ9/voTT6fDn3xr79iJbgvweOMnvPQa7fj8bDJRcfNKxL89IlATgfcZvBP2zn1yvXsLOx4RUvD2Y1fYk8phwkJSfTc/Um1p48h1tOJ/7b6hVqFy9i2a/dd2vZ895wnJ/cxyAi8kx77LEAuXLlws/Pj5YtW+Ln5/fYSdbt27d5//338fPzo0qVKnTs2JGDBw9ato8ePZrSpUunevn5+T2yzY0bN9KsWTPKly9Py5Yt2blz52PFJiIiktViEhJo9dVKfIsW5vdhfQka0JWImFh6rtkEpIwS+fzwMZp9uYK4pKRHtvVF0HE++HkvX7RtRsS7AxlZtwYdlq0j9O59ALqs+AGfgvm4MrwvwQO7cfnOXUZs3gHA5rMX2XM5lHPv9KRv9YqM/KMcYMjGn5j2Sj0c7B/r91gREeEfJFpZZciQIRw9epSZM2fy/fffU65cOXr06MG5c+cAOHXqFH379mX37t2W1+rVqx/a3r59+xg+fDidOnVi9erV1KlThwEDBljaExERsabLd+5RwbUA79XzxWhnSz5HEz2rVmD3pSsA9FqzmcDDwbxf/++fC5615yDjG9SiursbBoOBN8qXYVePN8jlYATg5M0Iks2QbDZjBmwMBhzt7QGw++O5qweLadr+8X5NyFkcjfa87PnsTm4lIvJvsOpPVZcuXeKXX37hm2++oUqVKgCMGTOGnTt3sn79egYOHMjZs2fp378/BQoUyFCbCxcupFGjRvj7+wMwcuRIjhw5wpIlS/jPf/7zxM5FREQkI0rnz8s6/9dSla08cZoqboUAGP9SLdxz52THhd8f2U50fAInwsKxNdjQYNF3nLgZjnc+FyY3qovzH4nW2Po1GbN1F/P2HSbJbKaGuxuTX64LQMOSxWnoWYxy8xZTOKczAa0aER2fwPiffmGDf9sncOYiIs8Xq97RcnFx4dNPP+WFF16wlBkMBsxmM3fu3OHixYvExcXh6emZofaSk5M5fPgwvr6+qcpr1KiRajiiiIhIdmA2m3l/2y9sOH2eGU3rA+CeO2eG9o2MjcUMzNxzkHktGnJ5aB/eKF+Gll+t4mLkHSDlDta79Xy5NXogZwb3BKD/+q0p22wMLGjZiNAR/TjYrwvVirgyeeevdKv8AreiY2iw6DtqTJ5FQEBAlp+3iMjzINN3tAICAmjYsCGlSpX6xwfPlSsX9erVS1W2ceNGLl++TJ06dTh9+jQGg4ElS5awc+dObGxsqFevHoMHDyZnzrT/EN29e5fo6GhcXV1TlRcsWJBr167943hFRESyyt3YOHqu2cSR0DC2dW9P+UIZG7nxgINtyj/hg2tWpVzB/AD0r1GZ/x78jR/PXOBFdzfGbf+Fm6MGYGdrg5PRnmmv+PHSou+Y26wBuXI4pGrv1K0Itp2/xK4eHakb+A1v16xK0zq1KDduHC+++CJlypTJmhN/ysXExKT6U7IP9U329iz1j9lsztBah5lOtD777DN8fHyyJNH6q0OHDvHuu+/SsGFDGjRowNy5c7GxsaFIkSIEBARw6dIlpk2bxunTp1myZAk2f1nXIzY2FgCj0Ziq3MHBgbi4uCyPV0RE5HGci7hNq6WrKJY7J3t7d04122BG5XcyUdDJMc2EGUnJyZiBy3fukmROJsmcjN0fA1jsbWwwGAyW57P+7J0ffmJGk/rY2dpwPOwWVdwKkdtkwtPTkx07dmA2m9Ps8zy7ePGitUOQh1DfZG/PSv/8Nd9IT6YTLQ8PD86cOfO3M/9l1tatWxk2bBgVK1Zk5syZAAwaNIhu3bqRK1cuALy9vSlQoAAdOnQgODiYihUrpmrDwSHl17n4+PhU5XFxcZhMmf9HTEREJKtFxsTSeMly6pcoxqetXsHG5u9/FX2YXtUqMGnHPmoWLcwLBfMTcCCI0Hv3aVXGE6OtLY729gz7cQfTG9fjblw8723bzatlvXA02qdqZ9mxU7jndqZWsZTp3b3yurDv91DyeZTg9OnT+Pr6UrZs2X903s+KmJgYLl68iIeHh75bZDPqm+ztWeqfs2fPZqhephOt+vXrM2vWLH766SdKlSpFvnz5Um03GAwMGDAgU21+9dVXTJo0iUaNGvHRRx9ZMkSDwWBJsh7w9vYG4Pr162kSrTx58uDo6EhYWFiq8rCwsDTDCUVERKxhyZHjXL5zj++Pn2LF8dOptkWOGfTIfXdfukLLr1ZxdEBXiuXJxdh6NcnlYKTz9xsIvXufMgXysrZzG4rkShle/0OXtry7ZRfFZ/wXk70dLUp7WibDeOBeXDxTd/7K5q7tLGVzmzeg95rNjNy6i0HDR1CzZs0sOvtnh8lkwtHR0dphSDrUN9nbs9A/GRk2CGAwZ3IswN+N0TYYDISEhGS4va+//poJEybQpUsX3n333VTDAYcOHcrt27cJDAy0lP3666+8+eab/PDDD+lOktGjRw/y5s3L9OnTLWVvvPEG3t7ejz3rYHBwMABl9mzRgsUiIvL8cC2CfZ8h1o4iW4mOjiYkJISyZcs+9V8WnzXqm+ztWeqfB7lB+fLlH1kv03e0Tp48+XgRpePChQtMnjyZRo0a0adPH8LDwy3bcuTIQYsWLejXrx+ffPIJzZs358KFC/znP/+hRYsWliTr3r17JCQkkDdvXgC6d+9O79698fHxwc/PjxUrVhASEsKkSZOyLG4REREREZFH+UfraN27d4+wsDCKFi2Kra0ttra2mdp/06ZNJCQksGXLFrZs2ZJqW5s2bZg6dSpz5swhICCAgIAAcubMScuWLRk8eLCl3qRJk9i/fz/bt28HoE6dOkyePJkFCxYwa9YsvLy8CAgIyPAU8SIiIiIiIv9UpocOQsrwvY8++ohjx45hMBhYvnw5CxcuxNXVlVGjRj2JOK1KQwdFROS5pKGDaTxLw5+eNeqb7O1Z6p8nNnRw79699OrVi8qVKzNs2DA++ugjAHx8fJg9ezaFChWie/fujxFy9mcoVQZz/oLWDkNERORfYXDJa+0Qsh2DwYDJZMrww/Ai8vzKdKI1e/ZsGjZsyJw5c0hMTLRMOtG7d2/u37/P8uXLn9lEy65BM2uHICIiIlZkMpnw8fH514+bZDZjq+RO5KmS6UQrJCTEMn37X3/NqV27NkuWLMmayLKhSbcucSlBCx+LiIjIv6e4vQNj8he3dhgikkmZTrRy5szJzZs309127do1cubM+Y+Dyq4uJcRxJj7G2mGIiIhIFrh/4hTnJs/kXnAIBnt78vrVxPO9oRjzunA/5DRnJ37EvaPHsDHloFDrZpQcPRgbu0d/dQr9diWnR/2H+heD0mxLionhaKc+uHVqi1u71gCYk5I4OXwctzb/hLFgAUpPG0ee6pUBiLl8hWO9h9Bh4wrIn+WnLyJPmM3fV0mtYcOGzJo1y/IQGKTc2bp+/ToBAQHUr18/K+MTERERyXJJsbH81m0guapUpNaBbby4ZQUJkbc5NXwc8RGRHO3cB5faNagdtIMqq74kfPtOrixa+sg2o06f5dzEjx667Uj7Htw98luq8oide7hz4Ai+v2ykSJf2nJs007LtzPhpeI55B1sHY5r2IiMj6d+/P0WLFsXJyQk3Nze6du3KlStXLHXCw8MZMmQIXl5eODs7U7hwYTp37syxY8dStRUXF8fo0aPx9PTE2dmZAgUK0LZt23TXRQ0PD8fR0ZFKlSo98rMQkcdItIYOHUq+fPlo3769JakaMmQITZo0wWAwMGSIZicSERGR7C3u6nWcy3rj8XYfbIz22LvkoXCn17m9/zA3VqzDVKIYxQf0wMbeHlPRIlT8MoCCzV95aHtJMTGcGDSKIt07pdkWuWc/QZ1649q2JQ5F3FJtM/xlaRyDbcpXs5ubtmNrMpG3bs10j9ehQwdu3brFgQMHiIqKIigoiLi4OBo1akRiYiKhoaFUrFiRy5cvs27dOu7evUtQUBAlS5akRo0aqZbVGTRoEHv27GHbtm3cv3+fM2fOULRoUfz8/Lh9+3aq43722Wc0bdqUa9eupVmaR0RSy/TQwdy5c7N8+XJWr17Nvn37uH37Njlz5qRLly689tprmEymJxGniIiISJZx9PSgwpL5qcpubtxKzhfKcjfoGE7eXpx69wNubf4JW0cTbu1bU6x/j4e2d2bsFPI18MOldg0uf/xZqm3OZb3x3b0R2xwO/L7wi1TbXOr44lLHl/0vtcZYqAClp75PUkwMF2cuoMIXCx56vN27dxMYGIirqysAhQoVYvbs2YwaNYrIyEiGDBlC8eLFWbZsGTY2KclbwYIFmThxIomJiXTr1o1Lly5hZ2fH7t276dKlCx4eHgDkyZOH6dOnc/fuXa5fv06ePHkASE5O5pNPPmHmzJmUKVOGGTNm0KhRowx93iLPo8dasNhoNNK+fXvat2+f1fGIiIiI/KvMZjMXZswnfOsOKi1bxLkPPuLW5u14T3qPUhNGEn3mPME938ZgNFKsd9c0+19ftYGosxfwnvo+dw4cSbPd3iXPQ49tsLGh9JSxlJ4y1lJ2/sO5uLZrTULEbU4MGsWxmDiq9B9I3759LXU6duxI37592bVrF/Xr16dGjRoUL16czz//nMTERFatWsXcuXMtSdaf9erVi6lTp7Jnzx78/Pzo2LEjEyZM4NSpUzRs2JAaNWrg7e3NokWLUu23du1akpKSaNWqFS+++CIlS5YkODj4b9cSEnleZXroIMC5c+cYNmwYtWvX5oUXXqBevXqMHDmSixcvZnF4IiIiIk9O4r37HO83jBurNlBp2SKcy5TCYDSSs+ILuLV/FRt7e5x9SlOk6xvc3LA5zf7R5y5yftocfOZN/duJMjIi+txFInfvo0i3jpwaOZ7CndvRfPkSxo0bx4kTJyz1Fi5cyPz587l8+TK9e/fGw8MDLy8vli5dyq1bt4iPj6dYsWLpHsPd3R2Aq1evAjB27FiWL19OVFQUQ4cOpXTp0hQpUoRZs2al2u/jjz9m4MCB2NnZ4e7uzmuvvcbMmTPTtC8iKR57wWIXFxfq169Pvnz5uHnzJjt27GDr1q0sXbqUMmXKPIlYRURERLJMzKXf+a37QHIUdqXquq8x5nUBwKlUSW7vPZCqrjkpGczmNG3c3LiFxDt3Odiswx/1kgDYVb4O3h+8S6HWmVuD88z4qXi9PxwbOzuiTp0jZ3kfjLly4unpyfHjxy1reNnY2ODv74+/vz9ms5mQkBC+/PJLunTpwsaNG7G3t+fSpUvpHiM0NBRIGW74QMuWLWnZsiWQ8oP6ypUrGTVqFLly5aJHjx6EhISwbds2Dh48aFlDNTY2loSEBCZPnoybm1vaA4k85zJ9R2vWrFlUr16dbdu2MWnSJIYMGcKUKVPYsmUL3t7eTJ48+UnEKSIiIpJlEu7cJahTb3JXqUiFLz6xJFkAru1e5f7Js1wOWIw5KYn7J89w9YtvKdSmRZp2ig/shV/IPuoG76Zu8G7KB84FoG7w7kwnWWHrNuHg5kruainTu5tKFOPOoSBiIyI5ffo0Xl5eAGzatAlnZ2ciIiKAlNmffXx8mDJlCpUrVyY4OJg2bdqwaNEikpOTAYiIiGD+/PlERUURGBhIoUKFqF27NiEhIeTIkSPVTISenp4MHz6cFi1acORIylDIjz/+mGbNmnHs2DGCgoIICgri5MmTlCxZknnz5mXqPEWeF5lOtE6ePEm3bt0wGlNPNerk5ETv3r05evRolgUnIiIi8iRcX76GuKvXCNuwmV0v1GanT03Ly8mrBJW/CyR8+y5+qVyf37r2p3DndhTp1hGA2/sPs9OnJrFXr2VZPIn3o7g0/zNKjnrbUlbqP6O5PP8zltVtwqBBg6hcOSUB8/Pzo1ChQnTv3p3g4GASEhK4d+8eS5cu5cyZMzRv3pzZs2dz/fp12rRpw4kTJ7h//z4rV67E29ubadOmsWjRIhwcHChTpgxVq1alT58+7N+/n9jYWKKjo9m4cSM//fQTbdq04e7du3zxxRf06tULd3f3VK9evXoREBBAVFRUln0WIs8Kg9mczn3wR2jcuDHdunWjY8eOabatX7+eWbNmsW3btiwLMDt4sGbYvPwOWrBYRERE/lWljCY+dfNOVXbt2jXGjx/Ppk2bCAsLw2g0UrNmTcaPH0+NGjWAlDWvPvjgA9asWcONGzfImTMn9erVIzQ0FKPRyPTp06lSpQp37tyx1Lt69Sq2trZUqlSJ0aNH07RpU+bOncsHH3zA1atXsbe3TxVHeHg4RYoU4cMPP+Stt9761z6T9ERHRxMSEkLZsmVxdHS0aiyS1rPUPw9yg7+bCCbTidbWrVsZM2YM48eP55VXXsH2j/Uffv31V0aPHs3w4cNp2rTpY4adPSnREhEREWtJL9H6J8xmMxs2bKBEiRKUK1cuy9q1tmfpi/yz6Fnqn4wmWhmaDKNMmTIYDAbLe7PZzJAhQ7C1tSVPnjzcu3eP+Ph4bG1tmTRp0jOXaImIiIg8KwwGAy1apH3eTESyVoYSrQEDBqRKtEREREREROThMpRoDRo06EnH8VQobu9g7RBERETkOaPvHyJPp8daWS8+Pp7z589z7969dLdXr179HwWVXY3JX9zaIYiIiMhzKMlsxlaji0SeKo+1YPHQoUOJjIwEUp7XgpTxvmazGYPBQEhISNZGmQ3Ex8cTExODyWSydijyJzExMVy4cIESJUqob7IZ9U32pv7JvtQ32Zu1+kdJlsjTJ9OJ1uTJk3FxcWH8+PHkyZPnCYSUfWVygkb5F5jNZmJiYtQ32ZD6JntT/2Rf6pvsTf0jIhmV6UTr8uXLzJo1iwYNGjyJeERERERERJ56NpndoXTp0pZhgyIiIiIiIpJWpu9ovfvuuwwbNgwbGxsqVKiQ7vjkwoULZ0lwIiIiIiIiT6PHnnXw3Xfffej2Z3EyDBERERERkYzKdKI1fvx4bG1teeeddyhQoMCTiElEREREROSplulE6/z588yZM4eXXnrpScQjIiIiIiLy1Mv0ZBjFixcnJibmScQiIiIiIiLyTMh0ovX2228za9YsfvnlF6Kiop5ETCIiIiIiIk+1TA8dnDFjBrdu3aJnz57pbjcYDJw4ceIfByYiIiIiIvK0ynSi1bx58ycRh4iIiIiIyDMj04nWwIEDn0QcIiIiIiIiz4xMJ1qhoaF/W+dZXbDYYDBYOwT5C4PBgMlkUt+IiIiISLaS6USrQYMGf/ul9llcsNhoNGIymawdhvyFyWTCx8fH2mE8dZLNydgYMj0XjoiIiIhkUKYTrcmTJ6dJtKKjozl06BD79u1j8uTJWRZcdvNj1I9EJEVYOwyRfySvbV6aODWxdhgiIiIiz7RMJ1qvvfZauuWdO3dm2rRprFu3jvr16//TuLKliKQIbibdtHYY8pT4/ejvrHp3FaHHQzGajFRqXYlWE1ph55D6sgtoF8D5fedTlcVHxVOza006zOpAQmwC6yas4+jao8Tdj6NgqYK0HNeSUnVLAbDjvzvYPH0zNnY2NBnZhNrdawOQlJDErEaz6LKwC4VKFXponB4eHly/fh07OzsMBgPJycnkzp3bck3b2NiwePFi5s2bx5kzZ7CxsaFChQqMGjUq1eQ4ZrOZhQsXsnDhQkJCQrC3t6dYsWIMHDiQXr16ZdXHKiIiIvJUyHSi9Sj169enf//+WdmkyFMpOTmZhR0X0vDthgxcN5C71+6y4LUFOOVzovHwxqnq9l3eN9X7fV/t48dpP9JkZMpdp3UT1nFh/wUGbxpMbrfc/PrVryzsuJDR+0Zjym1izXtrGLZjGJjho/ofUa19NRycHNjxyQ7Kvlz2kUnWAwEBAXTr1s3yPjg4mIYNG+Lo6Ejp0qUZPXo0q1ev5sUXXyQxMZFvv/2W1157jS1btuDn5wek/Niyd+9eZs2aRaNGjUhMTCQwMJAJEyawb98+AgMD/+GnKiIiIvL0yNJEKygoCDu7LG1S5KkUczuGu9fvYk42gzmlzGAwYDQZH7nfjTM3WDFyBX2/70tu19wAJMQm0HRUU1zcXQCo2bUm6yas4/eg3ynToEzKjuaUO0oYUo5z++ptDnx3gHe2vvNY8ZcvXx4/Pz8OHz7MzZs3qVixIr6+vkDK84pvvvkmly5dIjIyEoA1a9awYsUKQkJCKFmyJJAypLhevXrUq1ePGjVq8Prrr9O0adPHikdERETkaZPprGj06NFpypKTk7l27RoHDx7k9ddfz5LARJ5mTnmdqNevHmvGrmHt+2tJTkrmhWYvUK9/vUfu9/3w76n+RnU8a3payjrM6pCqzumdp4m5G0OR8kUwOhppO70tn3b4FIONgTfmvIHR0cjS/ktpPrb53yZ26UlISOCXX35h+/btTJgwgXLlytG4cWOaNGlCixYt8PX1pWLFiowdO9ayz6pVq6hdu7YlyfqzsmXLUqtWLZYvX65ES0RERJ4bmU60fv311zRlBoMBZ2dnevXqRd++fdPZS+T5kpycjNFkpO2HbfHt7MvN8zdZ9OYifpzyI83GNEt3n/P7znPp4CW6/LfLQ9u9eOAin3f/nCYjm5CveD4AanerTe1utS11Tm4/SVJiEiV9S7K422Junr9JyRolaTO5Dbb2tum2279/fwYPHmx57+7uztChQxk4cCAGg4FDhw6xYMECZs+ezblz53B2dsbf35/p06fj7OxMaGgorq6uD427cOHCGVoaQkRERORZkelEa/v27U8iDpFnSvD6YI6uO8q7v74LgFtZNxqPaMzKUSsfmmjt+XwPlVpXIlehXOlu3/vFXlaNWUXTUU15acBL6dZJjEtk7fi19Fzaky0zt+Do4sjwHcP5b7v/su+rfZaJMv5qwYIFqZ7R+qsKFSoQEBAAwM2bN9m6dSsjRozg/v37fPnll7i5uXH27NmH7n/hwgVNwy8iIiLPFS2kI/IERF6JJDEuMVWZrZ0tdsb0f9tISkwi+IdgqnWolmZbclIy373zHesnrqfHlz0emmQBbJ+3ncptKpO3aF6uhVyjaMWiGAwG3Cu6c+3Etcc6l2LFijF//nzL+wIFCtCxY0dGjBjBkSNHAGjXrh0HDhwgODg4zf5BQUEcPnyYtm3bPtbxRURERJ5GGbqjld5zWQ9jMBie6bW0RDKiTIMyrJ+4ni0zt9Dw7YZE/B7B5hmbqdquarr1Q4+HkhCbQIkXS6TZtmrMKkK2hjB0+1DyFs370GOGXw7nt/W/MXjzYAAKeBbg4oGL1Ohcg8uHL+PT6PHuKPn7+/PBBx9QvHhx6tati5OTE8ePHycwMNCSPLVo0YLOnTvTsmVL5s6dS8OGDYmOjubnn39m9uzZdOzYkRYtWjzW8UVERESeRhlKtNJ7LuuvIiMjiYmJUaIlAriWcaXXN734YdIPbJu7DVMuE9XaV6PxiJSp3UcUHUH7me2p1i7lDlb4xXAcXRyxz2Gfqp374ffZ/dlubGxtmFpraqptf94fYOWolbT6TyvLXbOXB7/Mkh5LGOM1Bu963tTqXuuxzmXSpEkULlyY8ePHc+rUKZKTkylRogQ9e/bk7bffttRbvHgxixYtYsqUKbz55psAFC9enDFjxtCnT5/HOraIiIjI08pgNpvN/6SBhIQEPvnkEz799FPy5MnD+PHjefnll7MqvmzhwXCo4OLBWrBYnnoFbAvQKVenJ36c6OhoQkJCKFu2LI6Ojk/8eJI56p/sS32Tval/si/1Tfb2LPXPg9ygfPnyj6z3jxa9CgkJYdSoUZw+fZrmzZszduxYcufO/U+aFBEREREReeo9VqKVmJjI/PnzWbhwIXny5OHjjz+mYcOGWR2biIiIiIjIUynTidaJEycYPXo0p06dolWrVrz33nvkypX+dNQiIiIiIiLPowwnWomJiXz88cd89tln5M2bl08++YSXXnr4NNPPIg97D1xsXKwdhsg/ktv23xneazAYMJlMGAyGf+V4kjnqn+xLfSMi8mzIUKJ1/PhxRo0axdmzZ3n11VcZM2YMzs7OTzq2bKeW6fFmbRN5HplMJi1SnI2pf7Iv9c2/w5ycjMFGy4mKyJOToUSrffv2JCcnkzNnTq5evUr//v0fWtdgMLBkyZIsCzA7SVy5FPPNG9YOQ0RERP4BQ4FC2L3W2dphiMgzLkOJVpUqVSz//XezwWd2tvjw8HCmTp3Krl27iIuLo3r16owYMQIvLy8gZWbDSZMmcezYMfLkyUOXLl3o0aPHI9vcuHEj8+bN4/fff8fDw4Phw4fj5+eXqbjSY755A65f/cftiIiIPIuOXr/JqM07OBx6A6OtLS97Fmd64/rkdzJZ6ly7d5/qAV8x+eW6vFm5XLrtxCYkMmbrLlaeOMO9+HhK58/LpJfrUL9EMQBC795nyI8/8fOF3zHa2tLhhdJMbFiHHPZ2JCUn03P1JtaePIdbTif+2+oVahcvAsD5iNu0+24te94bzvM3LkdE/m0ZSrS+/PLLJxZAv379sLGxYeHChTg6OjJnzhy6devGli1biI2NpXv37rz88stMmDCBoKAgJkyYQJ48eWjbtm267e3bt4/hw4czatQoatasyffff8+AAQNYvXo1np6eT+w8REREnmcxCQm0+mol/1e1PGs6teFefDz/t+pHeq7ZxOpOrwKQnGym64qN3IqOeWRbY7buYs/voezs2ZHCOZ34/MgxXv16Nb8N6IZ7rpy0/XYNhZydOD6oO2YzdP5+PQM3bOOzVxuz+exF9lwO5dw7Pfnq6AlGbt7B7l4pawcO2fgT016ph4P9P1rdRkQkQ6w6ODkyMhJ3d3cmTpxI+fLl8fT0pH///ty8eZMzZ86wbNkyjEYj48ePx9PTk7Zt29KtWzcWLlz40DYXLlxIo0aN8Pf3x9PTk5EjR1KuXLlndjijiIhIdnD5zj0quBbgvXq+GO1syedoomfVCuy+dMVS54MdeymSy5miuXI+sq2YxETGvVSLorlzYmtjQ4+qFXCwteXwtRucDo/kUOgN5jZrQD5HE/mdTExsWIdvfgvhTmwcdn88d/VgfI3tH+/XhJzF0WjPy57Fn8j5i4j8lVUTLRcXF2bOnEmpUqUAuHXrFoGBgbi6uuLl5cXBgwepXr06dnb/++XJ19eXCxcuEB4enqa95ORkDh8+jK+vb6ryGjVqcPDgwSd7MiIiIs+x0vnzss7/NUtiA7DyxGmquBUC4OcLl1l27BTzmv/9upsLWjaiSakSlvc/nb/Mnbh4KroWJMmcDICT0d6y3cZgICE5mfORd2hYsjgNPYtRbt5iPj9ynBlN6hMdn8D4n37ho8b1s+hsRUT+Xra5dz527FjLHaxPPvkER0dHrl+/jre3d6p6BQsWBCA0NJR8+fKl2nb37l2io6NxdXVNs8+1a9ee7AmIiIgIkPK89rjte9hw+jzburcn7H40vVZv5rsOLXF2MGaqrV9/D6Xj8vWMrV+TEi65SUpOxqdAPob9+DOzmr5EQnIyE3/eC6Q822VjY2BBy0YsaNnI0sZ7W3fTrfIL3IqOwf/7DURhoLfZka5du2b62fKYmJhUf0r2ob7J3p6l/jGbzRlagiPbJFpdu3alQ4cOfPPNNwwYMICvv/6a2NhYjMbUfyE7ODgAEBcXl6aN2NhYgHT3Sa++iIiIZK27sXH0XLOJI6FhbOvenhcK5qfFVysZUKMyVQoXylRbiw4FM/THnxn3Ui0G16oKpAwFXNmxNUN+/BmfeYsp5OzIOzWrsfHMBfKYHNK0cepWBNvOX2JXj47UDfyGt2tWpWmdWpQbN47ChQtTpEiRxzrPixcvPtZ+8uSpb7K3Z6V//ppvpCfbJFoPZhmcOHEiQUFBfPXVV+TIkYP4+PhU9R4kTI6OjmnaeJCEpbePyWRKU19ERESyzrmI27RauopiuXOyt3dn8juZuHz7LjsvXmH/letM2rEPgLtxcQzasI2VJ06zunObNO0kJSczaMM2Voec5fs3WtHwT89Vmc1mbsfG8U275pjsU4YP/njmAjmNRkrldUnT1js//MSMJvWxs7XheNgtqrgVIrfJhKenJ/fu3aNs2bKZOseYmBguXryIh4eHvltkM+qb7O1Z6p+zZ89mqJ5VE63w8HD27t1L06ZNsbW1BcDGxgZPT0/CwsJwdXUlLCws1T4P3hcqlPZXsTx58uDo6JjuPn8dTigiIiJZJzImlsZLllO/RDE+bfUKNjYpw2qK5cnFvbFvp6pbatZnjK1f86HTuw/78Wc2nbnI3t6dKZ4nV6ptBoOBbis30racN2Pr1eRc5G1Gb9lJ/xqVsLNN/ej5smOncM/tTK1iKXetvPK6sO/3UPJ5lOD06dP4+Pik+8NtRphMpsfeV54s9U329iz0T0aGDYKVE62wsDCGDh1Kvnz5qFmzJgAJCQmcOHGCBg0akD9/fr799luSkpIsidjevXspUaJEmuezIOWkq1Spwv79+2nXrp2l/Ndff6Vq1ar/zkmJiIg8h5YcOc7lO/f4/vgpVhw/nWpb5JhBj9x396UrtPxqFUcHdMXR3p5PDhzF1mCg0vzUMwbPb/kynSqU5et2LXhrwzbm7j1MLgcj3aq8wNh6NVPVvRcXz9Sdv7K56/++D8xt3oDeazYzcusuBg0fQeXKlf/hWYuIPJzBnNmnQLOQ2WymZ8+eXL16lQ8++IBcuXIREBDA7t27Wb16NQ4ODjRt2pQGDRrQs2dPfvvtN8aPH8+ECRNo0yZlqMG9e/dISEggb968AOzevZvevXtbFilesWIFS5cuZeXKlY+9jlZwcDAAZfZs0YLFIiIiTzvXItj3GfJYu0ZHRxMSEkLZsmWf+l/lnzXqm+ztWeqfB7lB+fLlH1nPqtO7GwwGZs+eja+vL4MHD6Zdu3bcuXOHpUuXUrhwYfLly8dnn33GhQsXaNOmDR9//DEjRoywJFkAkyZN4vXXX7e8r1OnDpMnT+abb76hTZs27Nu3j4CAAC1WLCIiIiIi/xqr3tF6WuiOloiIyDNEd7SeSeqb7O1Z6p+M3tHKNrMOPg0Mpcpgzl/Q2mGIiIjIP2Bwyfv4+xoMmEymDD8ML/8e9Y1kN0q0MsGuQTNrhyAiIiJWZDKZ8PHxsXYYko6H9U2S2Yytki+xAiVamTDp1iUuJWjhYxEREZGnQXF7B8bkL/73FUWeACVamXApIY4z8THWDkNERETE6iL37Of8tLlEn7uATY4cFGzeiJKjB2ObIwd3jwRzZvw0os6cw5jXheKDeuHWIe3i1ABJsXGcnzaHmxu3khQVjWNJD0qOfBuXWtXT1A15Zwyxodep/F0gAOakJE4OH8etzT9hLFiA0tPGkad6yrT9MZevsKLvUIYdPoqDg8OT+yBEHsKqsw6KiIiIyNMnPjyC4O6DKOzfjjq/7aLaD99ye99BLi9YRMKdu/zWfSCubVtQ57ddlP5wPGcnfsTdoOB02zo/bQ53DwVRZeUX1AnagdsbbQjuMYjYq9dS1bu2bDU31mxMVRaxcw93DhzB95eNFOnSnnOTZlq2nRk/jRpjR6ZJsiIjI+nfvz9FixbFyckJNzc3unbtypUrVyx1wsPDGTJkCF5eXjg7O1O4cGE6d+7MsWPHUrUVFxfH6NGj8fT0xNnZmQIFCtC2bVtCQkLSnGd4eDiOjo5UqlQpQ5+xPP2UaImIiIhIphjz5aXWoe24tWsNBgMJkXdIjovDPl9ebm7cir1Lboq8+QY2dna41HqRQq2bcfWL79JtKzk2Do93+pOjsCsGW1sKd2yLjdHIveD/JStRZ85xae6nuL3xWqp9Dba2f3mf8tX25qbt2JpMuNerneZ4HTp04NatWxw4cICoqCiCgoKIi4ujUaNGJCYmEhoaSsWKFbl8+TLr1q3j7t27BAUFUbJkSWrUqMGWLVssbQ0aNIg9e/awbds27t+/z5kzZyhatCh+fn7cvn071XE/++wzmjZtyrVr11K1Ic8uDR0UERERkUyzc3YCYG/NxsRfDyP3i1Vwa9eaCx99jFPpUqnqOpYqybXvVqXbTukpY1O9j9yzn8R793EuVxqApNhYTgwcSamJ73I3KJjocxcsdV3q+OJSx5f9L7XGWKgApae+T1JMDBdnLqDCFwvSPd7u3bsJDAzE1dUVgEKFCjF79mxGjRpFZGQkQ4YMoXjx4ixbtgwbm5TErWDBgkycOJHExES6devGpUuXsLOzY/fu3XTp0gUPDw8A8uTJw/Tp07l79y7Xr18nT548ACQnJ/PJJ58wc+ZMypQpw4wZM2jUqFEmPm15GumOloiIiIg8tho/r6Xmr5sx2NhwvN8wEqOisXU0papja8pBUvTfP+d+5/BvHO8/HI/BfTEVLQLAmfen4lK3JvleqpOmvsHGhtJTxlL78E9U37iMXBVf4NK8hbi2a01CxG3WtelM1apVCQgIsOzTsWNH+vbtS//+/Vm2bBmXLl3C1dWVzz//HBcXF1atWsWbb75pSbL+rFevXoSGhrJnzx5LWxMmTKBbt258+eWXnD59Gnt7exYtWkSZMmUs+61du5akpCRatWpFv3792L59u2UtJnl2KdESERERkcdmmyMHDoUKUnLU20Ts+CUlqYqJTVUnKSYWO6dHL1Ib+u1Kjvr3ofjAnni81RuAG6s3cD/kNCVHvJWhWKLPXSRy9z6KdOvIqZHjKftmR7Zv3864ceM4ceIEAAsXLmT+/PlcvnyZ3r174+HhgZeXF0uXLuXWrVvEx8dTrFixdNt3d3cH4OrVqwCMHTuW5cuXExUVxdChQyldujRFihRh1qxZqfb7+OOPGThwIHZ2dri7u/Paa68xc+bMNO3Ls0VDB0VEREQkU+4cCuLk8PFU/3E5NkZ7AJLjEzAY7XEq5Unkrn2p6kefOY9Taa902zInJXH6vcnc2rSdFz6dRd46vpZt11esJ+b8RX6p+lLKMeLiMSclsat8Har/uJwcRdxStXVm/FS83h+OjZ0dUafOkb9COXLnzo2npyfHjx/Hx8cHGxsb/P398ff3x2w2ExISwpdffkmXLl3YuHEj9vb2XLp0Kd1YQ0NDgZThhg+0bNmSli1bAnDu3DlWrlzJqFGjyJUrFz169CAkJIRt27Zx8OBBpk+fDkBsbCwJCQlMnjwZNze3tAeSZ4LuaImIiIhIpjiV8SY5Npbz0+aQHJ9A7JVQzk2aiVv7VynQ7GXib97i98CvSE5IIHLPAW6s+QHXdq+m29bZiR8RseMXqq5dmirJAqj45SfUPb6HusG7qRu8m2L9upO7WiXqBu9Ok2SFrduEg5sruaulTO9uKlGMsINHCA8P5/Tp03h5ebFp0yacnZ2JiIgAwGAw4OPjw5QpU6hcuTLBwcG0adOGRYsWkZycDEBERATz588nKiqKwMBAChUqRO3atQkJCSFHjhypZiL09PRk+PDhtGjRgiNHjgApd7OaNWvGsWPHCAoKIigoiJMnT1KyZEnmzZuXJf0h2ZMSLRERERHJFDsnRyosmU/U6bPsqdaAIx164FLXF6+xw7F3yUOFrwK4+cMWfqlcn1OjJuA1boRlXazb+w+z06cmsVevER8RydUvviP+5i32v9KWnT41La8bqzdkOJ7E+1Fcmv8ZJUe9bSkr9Z/RHJkbQOnSpRk0aBCVK1fGz8+PQoUK0b17d4KDg0lISODevXssXbqUM2fO0Lx5c2bPns3169dp06YNJ06c4P79+6xcuRJvb2+mTZvGokWLcHBwoEyZMlStWpU+ffqwf/9+YmNjiY6OZuPGjfz000+0adOGu3fv8sUXX9CrVy/c3d1TvXr16kVAQABRUVFZ3T2STRjMZrPZ2kFkdw8eVpyX30ELFouIiIg8JUoZTXzq5p2q7Nq1a4wfP55NmzYRFhaG0WikZs2ajB8/nho1agApa1598MEHrFmzhhs3bpAzZ07q1atHaGgoRqOR6dOnU6VKFe7cuWOpd/XqVWxtbalUqRKjR4+madOmzJ07lw8++ICrV69ib2+fKo7w8HCKFCnChx9+yFtvZewZtKdZdHQ0ISEhlC1bFkfHRz+vl909yA3Kly//yHpKtDJAiZaIiIjI0ye9ROufMJvNbNiwgRIlSlCuXLksa/d58DwmWpoMQ0REREQkAwwGAy1atLB2GPKU0DNaIiIiIiIiWUx3tDKhuL2DtUMQERERkQzSdzexJiVamTAmf3FrhyAiIiIimZBkNmNrMFg7DHkOaehgBsXHxxMTo4kwspuYmBhOnDihvsmG1DfZm/on+1LfZG/qn+zrYX2jJEusRYlWJmiCxuzHbDYTExOjvsmG1DfZm/on+1LfZG/qn+xLfSPZjRItERERERGRLKZES0REREREJIsp0RIREREREcliSrRERERERESymBItERERERGRLKZES0REREREJIsp0RIREREREcliSrRERERERESymBItERERERGRLKZES0REREREJIsp0RIREREREcliSrQywWAwWDsE+QuDwYDJZFLfiIiIiEi2YmftAJ4WRqMRk8lk7TDkL0wmEz4+PtYOI9tLNidjY9DvKiIiIiL/FiVamfBj1I9EJEVYOwyRTMlrm5cmTk2sHYaIiIjIc0WJViZEJEVwM+mmtcOQbOr3o7+z6t1VhB4PxWgyUql1JVpNaIWdQ9rLLKBdAGd3n8XG7n93mbov7k7Zl8umqrf3i718N/g7ZkfMtpTt+O8ONk/fjI2dDU1GNqF299oAJCUkMavRLLos7EKhUoUeGauHhwfXr1/Hzs4Og8FAcnIyuXPnpnPnzkybNg0bGxsWL17MvHnzOHPmDDY2NlSoUIFRo0bRvHlzSztms5mFCxeycOFCQkJCsLe3x8fHhz59+vD6668/zscoIiIi8kxQoiWSBZKTk1nYcSEN327IwHUDuXvtLgteW4BTPicaD2+cpv7vQb/T9/u+eNX2emib10Kusfq91anKYu/Fsua9NQzbMQzM8FH9j6jWvhoOTg7s+GQHZV8u+7dJ1gMBAQF069bN8j44OJiGDRvi6OhI6dKlGT16NKtXr+bFF18kMTGRb7/9ltdee40tW7bg5+cHQOfOndm7dy+zZs2iUaNGGI1GNm7cyIABA9i2bRtvvfVWhmIRERERedYo0RLJAjG3Y7h7/S7mZDOYU8oMBgNGkzFN3fBL4URHRuNewf2h7cVHx/NFzy/w6+PHlhlbLOU2tn/cATOn3E3CkHKc21dvc+C7A7yz9Z3HPofy5cvj5+fH4cOHuXnzJhUrVsTX1xdIeUbxzTff5NKlS0RGRgKwZs0aVqxYQUhICCVLlrS006pVK7y8vKhQoQJVq1albNmy6R5PRERE5FmmREskCzjldaJev3qsGbuGte+vJTkpmReavUC9/vXS1L18+DIOzg4s6bGEy0cuk7NATur3r4+vv6+lzvfDv8ensQ/e9bxTJVpGRyNtp7fl0w6fYrAx8MacNzA6GlnafynNxzZPN7HLiISEBH755Re2b9/OhAkTKFeuHI0bN6ZJkya0aNECX19fKlasyNixYy37rFq1itq1a6dKsh7w8fHB19eXrVu30rNnz8eKSURERORppkRLJAskJydjNBlp+2FbfDv7cvP8TRa9uYgfp/xIszHNUtVNjE/Eo7oHzd9rjltZN87sOsPibovJ4ZyDSq9W4uCyg9w4fYMOczpwft/5NMeq3a02tbvVtrw/uf0kSYlJlPQtyeJui7l5/iYla5SkzeQ22NrbPjTm/v37M3jwYMt7d3d3hg4dysCBAzEYDBw6dIgFCxYwe/Zszp07h7OzM/7+/kyfPh1nZ2dCQ0NxdXV9aPtubm5cvXo1E5+iiIiIyLND8z2LZIHg9cEcXXeUOv9XBzsHO9zKutF4RGN2L9qdpm71DtXpu7wv7hXcsbW3pUyDMlTvUJ0jq45w48wN1k1Yx5ufvYmt3cOTpAcS4xJZO34tr015jS0zt+Do4sjwHcO5deEW+77a98h9FyxYwO3bty2vY8eOMWbMGMuaZBUqVCAgIICzZ88SFhbGp59+yvr16+nXrx+QkkhdunTpoe1fvHiR/Pnz/+05iIiIiDyLlGiJZIHIK5EkxiWmKrO1s8XOmPam8b6v9hG0OihVWWJcIvYme46uPUrMnRim15vOKI9RLOy4EIBRHqM49P2hNG1tn7edym0qk7doXq6FXKNoxaIYDAbcK7pz7cS1xz6fYsWKMX/+fMv7AgUK0LFjR0aMGMGRI0cAaNeuHQcOHCA4ODjN/keOHCEoKIgGDRo8dgwiIiIiTzMlWiJZoEyDMty9cZctM7eQnJTMrYu32DxjM1XbVU1TN/ZuLN+P/J4rv10hOTmZ45uPc2jFIWp2rckrQ1/hwysfMvXiVKZenEqvb3oBMPXiVKq+nrqt8Mvh/Lb+N14a8BIABTwLcPHARZISkrh8+DL5Szz+3SR/f38++OAD1q9fz507d0hMTOTo0aMEBgbStm1bAFq0aEHnzp1p2bIla9euJSoqiqioKNasWUPr1q1p164ddevWfewYRERERJ5mekZLJAu4lnGl1ze9+GHSD2ybuw1TLhPV2lej8YiUqd1HFB1B+5ntqdauGvX61SM+Op7ALoHcv3WffMXz0XlBZzxrembqmCtHraTVf1pZ7pq9PPhllvRYwhivMXjX86ZW91qPfT6TJk2icOHCjB8/nlOnTpGcnEyJEiXo2bMnb7/9tqXe4sWLWbRoEVOmTOHNN98EUibCmDBhAu3bt+fkyZOPHYOIiIjI08xgNpvN1g4iu3swNCq4eLAWLJanTgHbAnTK1elfP250dDQhISGULVsWR0fHf/348mjqn+xLfZO9qX+yL/VN9vYs9c+D3KB8+fKPrKehgyIiIiIiIllMiZaIiIiIiEgWU6IlIiIiIiKSxTQZRiZ42HvgYuNi7TBEMiW3bW6rHNdgMGAymSzrckn2ov7JvtQ32Zv6R0QySolWJtQyPf4sbiLPG5PJhI+Pj7XDkIdQ/2Rf6pvs7XnpH3NyMgYbDXwS+SeUaGVC4sqlmG/esHYYIiIiIk+MoUAh7F7rbO0wRJ56Vk+0wsPDmTp1Krt27SIuLo7q1aszYsQIvLy8ABg9ejQrV65MtU+hQoXYuXPnQ9vcuHEj8+bN4/fff8fDw4Phw4fj5+f3j2M137wB16/+43ZERETk+XP0+k1Gbd7B4dAbGG1tedmzONMb12fc9l/4+reQVHVjEhNpWLIYG7q0TdOOy6R5qd4nm83EJCbyRdtmvFG+DLEJiby7dRfLj50iJjGRqoULMadZQ8oUyEtScjI9V29i7clzuOV04r+tXqF28SIAnI+4Tbvv1rLnveE4P7mPQeS5YfVEq1+/ftjY2LBw4UIcHR2ZM2cO3bp1Y8uWLZhMJk6dOkXfvn3x9/e37GNra/vQ9vbt28fw4cMZNWoUNWvW5Pvvv2fAgAGsXr0aT8/MLQgrIiIikhViEhJo9dVK/q9qedZ0asO9+Hj+b9WP9FyzidWdXmV+y5ctdbecvUiXFT/wYeN66bYVOWZQqvfdV27kZlQ0r/t4AzBwwzbOhkeyv68/eU05GLFpB28sW0fQgK5sPnuRPZdDOfdOT746eoKRm3ewu1fKWotDNv7EtFfq4WBv9a+HIs8Eqw6+jYyMxN3dnYkTJ1K+fHk8PT3p378/N2/e5MyZMyQlJXH27FnKly9PgQIFLK+8efM+tM2FCxfSqFEj/P398fT0ZOTIkZQrV44lS5b8i2cmIiIi8j+X79yjgmsB3qvni9HOlnyOJnpWrcDuS1dS1bsVFUPXlRuZ2fQlyhXM/7ftfnHkONvOX2ZJ22bY2doQdj+apUdPsPDVxrjldMbBzo7JjfxY3KYJZrMZuz+euzL/sb/tH+/XhJzF0WjPy57Fs/S8RZ5nVv3JwsXFhZkzZ1re37p1i8DAQFxdXfHy8uLixYvExcVl+E5UcnIyhw8fZtSoUanKa9SowZYtW7I0dhEREZGMKp0/L+v8X0tVtvLEaaq4FUpV9u7WnVQtXIhOFcr+bZt3YuMYsXkHc5s3JJ+jCYDD126QJ4cDv165xuvfruVWVDS1ihVhRpP6GAwGGpYsTkPPYpSbt5jCOZ0JaNWI6PgExv/0Cxv80w5TFJHHl23uDY8dO5Zly5ZhNBr55JNPcHR05PTp0xgMBpYsWcLOnTuxsbGhXr16DB48mJw5c6Zp4+7du0RHR+Pq6pqqvGDBgly7du3fOhURERGRhzKbzYzbvocNp8+zrXt7S/mFyDssPRrCL38M5fs7H/96hOJ5ctGunLelLCImltuxcaw6cYat3dphtLXl7R+20+ab1ezv44+tjQ0LWjZiQctGln3e27qbbpVf4FZ0DP7fbyAKA73NjvTt2zfrTlrkOZRtEq2uXbvSoUMHvvnmGwYMGMDXX3/NmTNnsLGxoUiRIgQEBHDp0iWmTZvG6dOnWbJkCTZ/mXY0NjYWAKPRmKrcwcGBuLi4f+1cRERERNJzNzaOnms2cSQ0jG3d21O+UAHLts+PHKNWscJUciv4t+2YzWYWHw7m/ZdqpVrTy8HWliSzmWmv+FHAyRGA6Y3rUWR6AKduReJTMF+qdk7dimDb+Uvs6tGRuoHf8HbNqjStU4ty48bx4osvUqZMmSw68ycvJiYm1Z+SvTxL/WM2mzO0ll62SbQezDI4ceJEgoKC+Oqrr5g8eTLdunUjV65cAHh7e1OgQAE6dOhAcHAwFStWTNWGg4MDAPHx8anK4+LiMJlM/8JZiIiIiKTvXMRtWi1dRbHcOdnbuzP5nVJ/N1l14gxDalXLUFsHr14n7E8TYDxQtkBKIhWXlGQpS0pOeSLLbHky63/e+eEnZjSpj52tDcfDblHFrRC5TSY8PT3ZsWMHZnPafbK7ixcvWjsEeYRnpX/+emMnPVZNtMLDw9m7dy9Nmza1zCRoY2ODp6cnYWFhGAwGS5L1gLd3yl8o169fT5No5cmTB0dHR8LCwlKVh4WFpRlOKCIiIvJviYyJpfGS5dQvUYxPW72CjU3qX8PDo2M4eSuCOn9Mtf53frkcShW3Qjga7VOV+xTMR93iRRiwbivfv9GaHHa2jNi8g8puBdNMrrHs2CnccztTq1jKMb3yurDv91DyeZTg9OnT+Pr6Urbs3z8rll3ExMRw8eJFPDw89AN7NvQs9c/Zs2czVM+qiVZYWBhDhw4lX7581KxZE4CEhAROnDhBgwYNGDp0KLdv3yYwMNCyT3BwMPC/O2B/ZjAYqFKlCvv376ddu3aW8l9//ZWqVas+4bMRERERSd+SI8e5fOce3x8/xYrjp1NtixwziIuRdwAokivtCla7L12h5VerODqgK8XypPwAfSHyNoXTqQuwsuOrvLtlF9UDvuRuXDz1ShRlxRutU9W5FxfP1J2/srnr/74vzW3egN5rNjNy6y4GDR9h+W72tDGZTDg6Olo7DHmIZ6F/MjJsEMBgtuI9YbPZTM+ePbl69SoffPABuXLlIiAggN27d7N69WpOnTpFv379ePvtt2nevDkXLlzgP//5D5UqVWLGjBkA3Lt3j4SEBMuU77t376Z3796WRYpXrFjB0qVLWbly5WOvo/UguSuzZ4sWLBYREZFnm2sR7PsMsXYUmRYdHU1ISAhly5Z96r/IP4uepf55kBuUL1/+kfWsekfLYDAwe/ZsZsyYweDBg7l37x7VqlVj6dKlFC5cmMKFCzNnzhwCAgIICAggZ86ctGzZksGDB1vamDRpEvv372f79u0A1KlTh8mTJ7NgwQJmzZqFl5cXAQEBWqxYRERERET+NVa9o/W00B0tEREReW7ojpY8Ac9S/zwVd7SeNoZSZTDn//spV0VERESeVgaXvNYO4bEYDAZMJlOGn58RedKUaGWCXYNm1g5BRERERNJhMpnw8fGxdhjZSpLZjK0ST6tRopUJk25d4lKCFj4WERERkeytuL0DY/IXt3YYzzUlWplwKSGOM/FP/2rWIiIiIvJ4Ivfs5/y0uUSfu4BNjhwUbN6IkqMHY5sjB1e/+JYri5YSF3YLh4L5KdK9M+5d30i3naTYOM5Pm8PNjVtJiorGsaQHJUe+jUut6v/bPnU2Yes3kxwXR87yPpT6z2icvEpgTkri5PBx3Nr8E8aCBSg9bRx5qlcGIObyFY71HkKHjSvgT0uneXh4cP36dezsUn/9j4qKwmg0EhkZaXl2KjY2lvz585M7d26uXLliGY55/vx5PD09CQ4O5oUXXgBg/vz5DBw4kJkzZ/LOO+9k6Wf9tLOxdgAiIiIiIk+D+PAIgrsPorB/O+r8totqP3zL7X0HubxgEbe27uDCjAX4zJuG34m9lJ0zhfOTZxG550C6bZ2fNoe7h4KosvIL6gTtwO2NNgT3GETs1WsAnHlvEveOhVDth2+pfXA7jl4lON5/GAARO/dw58ARfH/ZSJEu7Tk3aaal3TPjp+E55h1sHYxpjhkQEMD9+/fTvAwGA7t377bU27p1Kx4eHty/f59ff/3VUr5lyxY8PDwsSRakJFr9+vVjzpw5JCYm/rMP+BmjREtEREREJAOM+fJS69B23Nq1BoOBhMg7JMfFYZ8vL/lfrofvLxvJWd6H5MREEiJug8GAXa6c6baVHBuHxzv9yVHYFYOtLYU7tsXGaORecAjxtyK4vmoDZaZPwKFgAWwcjHiOGkzZmR9gNpsx2Nqmastgm/KV/uam7diaTOStm/HFpp2cnKhfv75lqSSA1atX06JFC5o0acLq1ast5Vu3bqVly5aW99u2bSMsLIwZM2aQnJzM999/n+HjPg+UaImIiIiIZJCdsxMAe2s25mDj1zEWLJCSeP2xLfrcRXaWrkFw94EU9m9HzhfKpNtO6SljyfdSHcv7yD37Sbx3H+dypbkXfAK7XDm5eySY/Y1e45eqLxHyzhjsXfJgMBhwqeOLSx1f9r/UmmvLVuP1/nCSYmK4OHMBXu8Py/Q5NW/e3JJoJScns27dOlq3bk2rVq0siVZycjLbt2+nVatWlv3mzZtHr169MJlM9O/fnxkzZmT62M8yraOVAQ/myp+X30HPaImIiIgISbGxJN65S8jb72Lj4ECFJfMBSE5IAOB+yGmO9RqMe7dOFOvX/ZFt3Tn8G8H/Nwj3/+uMx1u9ubF6AyFD3ydfg7qUnvo+Nvb2nBk3lfshZ6i24Zs0d7QAzn84F/u8LrjUrsGZcVOxj4ljYv+B9O3bF0h5RissLAyjMfWQwqtXr3Ljxg28vb0JDw8nODiY119/ndDQUO7cuUPBggU5duwY9+7do2HDhty6dQt7e3suXbpE6dKlOXv2LO7u7kRERODu7s7GjRupV69emviex3W0dEdLRERERCSTbHPkwKFQQUqOepuIHb+QcOcuADb29tjY25OrQjncu3fixpqNj2wn9NuVHPXvQ/GBPfF4qzcABqMRkpLwHDMEY7682OXKied7Q4k6eZro8xfTtBF97iKRu/dRpFtHTo0cT+HO7Wi+fAnjxo3jxIkTlnoLFizg9u3bqV5OTk6ULFmSUqVKsWPHDlavXk3Lli2xsbHBxcWFunXrsnHjRrZs2ULjxo2xt7e3tJWQkEClSpXInz8/3t7eJCQk8NFHH2XRJ/z0U6IlIiIiIpIBdw4F8WuDV0mOT7CUJccnYDDac+3rFRwfMCJV/eT4BOzz5Eq3LXNSEqdGT+TCh/N44dNZFO3ZxbLNqVTJlDrx8X+qn/zHf6QdjHZm/FS83h+OjZ0dUafOkbO8D8ZcOfH09OT48eMZOrfmzZuza9cuNmzYQOvWrS3lrVq1Ytu2bWzdutUybDA2NpbAwEACAwMJCgqyvNatW8eGDRs4efJkho75rFOiJSIiIiKSAU5lvEmOjeX8tDkkxycQeyWUc5Nm4tb+VVzq1ODWlp8IW78Jc3Iydw4e4criryns3y7dts5O/IiIHb9Qde1S8tbxTX2cUp7kfrEqp979gPiISBKjojk3aQbOL5TFydsrVd2wdZtwcHMld7WU6d1NJYpx51AQsRGRnD59Gi+v1PUfpnnz5qxcuZLff/+dl19+2VLeunVr9u/fz/79+2natCkAX3/9NQaDgc6dO+Pu7m55NWnShPLlyzNz5syHHea5okRLRERERCQD7JwcqbBkPlGnz7KnWgOOdOiBS11fvMYOJ2d5H8ot+IhLHweyu0JdTo+ZRKlxwynYojEAt/cfZqdPTWKvXiM+IpKrX3xH/M1b7H+lLTt9alpeN1ZvAKD8Z7Nx8vbiYLMO7K3RiKSoGF5YODtVPIn3o7g0/zNKjnrbUlbqP6O5PP8zltVtwqBBg6hcuXKGzq1OnTqEh4fTqFEjcuTIYSkvUaIEBQsWpHLlyuTLlw9ImdK9c+fOlmGEf9a7d2++/PJLwsLCMvXZPos0GUYGaDIMEREREXmalDKa+NTN29phWGgyDBEREREREfnHlGiJiIiIiIhkMSVaIiIiIiIiWczO2gE8TYrbO1g7BBERERGRv6XvrdanRCsTxuQvbu0QREREREQyJMlsxtZgsHYYzy0NHcyg+Ph4YmI042B2ExMTw4kTJ9Q32ZD6JntT/2Rf6pvsTf2Tfalv0lKSZV1KtDJBM+FnP2azmZiYGPVNNqS+yd7UP9mX+iZ7U/9kX+obyW6UaImIiIiIiGQxJVoiIiIiIiJZTImWiIiIiIhIFlOiJSIiIiIiksWUaImIiIiIiGQxJVoiIiIiIiJZTImWiIiIiIhIFlOiJSIiIiIiksWUaImIiIiIiGQxJVoiIiIiIiJZTImWiIiIiIhIFlOiJSIiIiIiksWUaGWCwWCwdgjyFwaDAZPJpL7JhtQ3IiIi8jyzs3YATwuj0YjJZLJ2GPIXJpMJHx8fa4ch6cjKvkk2J2Nj0O9CIiIi8vRQopUJP0b9SERShLXDEHmu5LXNSxOnJtYOQ0RERCRTlGhlQkRSBDeTblo7DJEsdf/WfWY3nk2HOR0oVacUABcPXmTlqJVcP3Ud53zOvDL0FXy7+Ka7f3xMPKvHrCb4h2AS4xJxr+hOm0ltKFyucJq6X/X9isirkQxaNwiA5KRkvh74NcEbgsntlps35rxBSd+SANy6eItFXRYx+afJ4PS/Njw8PLh+/Tp2dnYYDAaSk5PJnTs3nTt3Ztq0adjY2LB48WLmzZvHmTNnsLGxoUKFCowaNYrmzZtb2jGbzSxcuJCFCxcSEhKCvb09Pj4+9OnThzfffDOrPl4RERF5Tmksjshz7Py+88xuPJtbF25ZyqJvR/Nph0+p/kZ1plyYwhtz32DVmFVcOnQp3TZ+nPYjN8/dZNTeUUw8NZHC5QoT2CUwTb19X+3j0PeHUpWd3H6SC/suMC54HLX/rzZrxq6xbFs5aiWt/9Maewf7NG0FBARw//597t27R1RUFJs2bWLJkiVMmDCBr7/+mtGjR7NgwQLu3LnDzZs36dWrF6+99ho7d+60tNG5c2emTJnCmDFjuHHjBmFhYYwcOZIxY8bQo0ePTH+WIiIiIn+mO1oiz6n93+xn45SNtBzfki96fmEpP7r2KE55najbsy4A3n7eVG1Xld2BuyletXiadm6cvkFycjKYATPY2NpgNBlT1bl+8jqbP9pMzTdrcuPMDUu5je0fv/WYU7//bcNvGB2NlH6pdIbOpXz58vj5+XH48GFu3rxJxYoV8fVNuQNnNBp58803uXTpEpGRkQCsWbOGFStWEBISQsmSJS3ttGrVCi8vLypUqMDrr79O06ZNM3R8ERERkb9SoiXynCrToAxV21XF1s42VaJ1/eR13Mq6parrWtqVfV/tS7edlwa8xOKuixnjNQYbWxuc8jkxYM0Ay/b4mHiW9FjC69Nf59KhS6kSLe/63njX92ZS9UkpQwdnv0F8dDwbJ2+k7/d9M3QeCQkJ/PLLL2zfvp0JEyZQrlw5GjduTJMmTWjRogW+vr5UrFiRsWPHWvZZtWoVtWvXTpVkPeDj40OtWrVYvny5Ei0RERF5bEq0RJ5TuQrlSrc87n4cRqfUd6SMJiPxUfHp1k9OTKZCywo0Ht6YHDlzsHbcWgL9AxmxawT2OexZMWIFpV8qjU8jnzTDD21sbOgwqwMdZnWwlK2fuJ4anWsQFRHFkp5LSI5O5m7fu/Tt+7/Eq3///gwePNjy3t3dnaFDhzJw4EAMBgOHDh1iwYIFzJ49m3PnzuHs7Iy/vz/Tp0/H2dmZ0NBQXF1dH/rZFC5cmNDQ0IduFxEREfk7SrREJBWjo5GYazGpyuJj4nFwdkhTNykhic+7f07v73qTp3AeANpOa8voEqM59fMpYu/FEno8lLd/fDtDx75x5ganfj7F4E2Dmd14NvX718evsR9jqo3Bz8/PMl38ggUL6Nat20PbqVChAgEBAQDcvHmTrVu3MmLECO7fv8+XX36Jm5sbZ8+efej+Fy5c0LIBIiIi8o9oMgwRScWtrBvXTl5LVXb9VNrhhABxUXFE344mMT7RUmawNWCwMWBrb8uBbw8QdjaM97zfY5THKLbN2caFfRcY5TGKyCuRadpbOXIlbSa1wdbOlush1ylasSiOuR3x9PTk+PHjGYq/WLFizJ8/3/K+QIECdOzYkREjRnDkyBEA2rVrx4EDBwgODk6z/5EjRzh8+DBt27bN0PFERERE0qNES0RSqdCyAvfC7vHzJz+TlJDEmV1nOLT8EDU610hT1zGPIyV9S7Ju/Dru3bxHQmwC68avwymvEyV9S9JvRT+mXZ7G1ItTmXpxKg3fbkgJ3xJMvTgVF3eXVG0dXnmYPEXyWKZ3z18yPxf2X+BexD1Onz6Nl5dXhuL39/fngw8+YP369dy5c4fExESOHj1KYGCgJXlq0aIFnTt3pmXLlqxdu5aoqCiioqJYs2YNrVu3pmPHjrRo0eIffpIiIiLyPNPQQRFJxSmvE/1W9mPV6FVsnLIR5/zOvDb1NUrVTVlj69zec/y3/X8ZvXc0Lu4udP+8O2vGreHDuh+SlJCERzUP+n7fFwentEMNHyb2XixbZm5JNYnG69Nf55tB37Du/XUMe2sYlStXzlBbkyZNonDhwowfP55Tp06RnJxMiRIl6NmzJ2+//b8hjIsXL2bRokVMmTLFsm6Wj48PEyZMeOSwRBEREZGMMJjNZrO1g8juHgwvCi4erAWLRf5lBWwL0ClXJ2uH8cyJjo4mJCSEsmXL4ujoaO1w5E/UN9mb+if7Ut9kb89S/zzIDcqXL//Ieho6KCIiIiIiksWUaImIiIiIiGQxPaOVCR72HrjYuPx9RRHJMrltc1s7hGeSwWDAZDJhMBisHYr8hfome1P/iEhGKdHKhFqmWtYOQUQkS5hMJq0Vlk2pb7I39c+/y5ycjMFGA7Dk6aREKxMSVy7FfPOGtcMQEREReeYZChTC7rXO1g5D5LEp0coE880bcP2qtcMQEREReWxHr99k1OYdHA69gdHWlpc9izO9cX3yO5nYePo872//hXMRtynhkpux9WvyatlSf9vmokPB9F23hfjxQ9Jsi45PoPEX39OragXerFwOgKTkZHqu3sTak+dwy+nEf1u9Qu3iRQA4H3Gbdt+tZc97w3HO2lMX+Vdlq3uxFy5coHLlyqxcudJSFhISgr+/P5UqVaJ+/foEBgb+bTsbN26kWbNmlC9fnpYtW7Jz584nGbaIiIjIUyEmIYFWX63Et2hhfh/Wl6ABXYmIiaXnmk0cCb3B69+upV/1SoSNHMCcZg3osWoTOy78/sg2j4fdYtimnx+6rcHiZfx65Vqq8s1nL7Lncijn3ulJ3+oVGbl5h2XbkI0/Me2VejjY636APN2yTaKVkJDAsGHDiI6OtpRFRkbSvXt3PDw8WLFiBYMGDWLOnDmsWLHioe3s27eP4cOH06lTJ1avXk2dOnUYMGAA586d+zdOQ0RERCTbunznHhVcC/BePV+MdrbkczTRs2oFdl+6wvLjp6ldrAj/V7U8drY21CnuTscKZfjvwaMPbS86PgH/7zcwsEaVNNt+On+Zxku+p0slH4rlzplqm90fz109WMzV9o/3a0LO4mi052XP4llzwiJWlG0SrXnz5uHk5JSqbNmyZRiNRsaPH4+npydt27alW7duLFy48KHtLFy4kEaNGuHv74+npycjR46kXLlyLFmy5EmfgoiIiEi2Vjp/Xtb5v2ZJbABWnjhNFbdCJJuTcTTap6pvYzBw6lbEQ9t764ftNPMuScOSxdJsq+BagLODezKgRmUMpJ6lsWHJ4jT0LEa5eYv5/MhxZjSpT3R8AuN/+oWPGtf/Zycpkk1ki0TrwIEDfPfdd0ybNi1V+cGDB6levTp2dv+7dezr68uFCxcIDw9P005ycjKHDx/G19c3VXmNGjU4ePDgkwleRERE5ClkNpt5f9svbDh9nhlN69O6TCm2nrvEyhOnSUxKZs/lqyw7doqYhMR091969AQnb4Yz4aXa6W7P52gix0OG/9nYGFjQshGhI/pxsF8XqhVxZfLOX+lW+QVuRcfQYNF31Jg8i4CAgCw7X5F/m9UHv969e5cRI0bw3nvv4ebmlmrb9evX8fb2TlVWsGBBAEJDQ8mXL1+atqKjo3F1dU2zz7VrqccGi4iIiDyv7sbG/fFcVhjburenfKECACxu04SJP++l/7qt1C5WhK6VyrH7ctqJwE7diuC9rbvZ/n8dsLP957/bn7oVwbbzl9jVoyN1A7/h7ZpVaVqnFuXGjePFF1+kTJkyf9tGTExMqj8le3mW+sdsNmdoLT2rJ1rjx4+nUqVKtGzZMs222NhYjEZjqjIHBwcA4uLi0q0PpLtPevVFREREnjfnIm7TaukqiuXOyd7encnvZAIgIjoGn4L5ONK/q6Vup+XrqVq4UJo2Vp44Q2RsLC8GfAVAYnIyAAWmzGdu8wZ0rFA2UzG988NPzGhSHztbG46H3aKKWyFym0x4enqyY8cOzGbz3zfyh4sXL2bq2PLvelb656/5RnqsmmitXr2agwcPsm7dunS358iRg/j4+FRlDxImR0fHNPUfJGHp7WMymbIiZBEREZGnVmRMLI2XLKd+iWJ82uoVbGz+96v82YjbNF7yPT/3eINyBfKxMuQMG06dZ0/vTmnaGe1Xg9F+NSzvd1z4nUZLlnNz9IBMx7Ts2CnccztTq1jK9O5eeV3Y93so+TxKcPr0aXx9fSlb9u8Tt5iYGC5evIiHh4e+92VDz1L/nD17NkP1rJporVixgvDwcOrXr5+qfNy4cQQGBlK4cGHCwsJSbXvwvlChtL+u5MmTB0dHx3T3+etwQhEREZHnzZIjx7l85x7fHz/FiuOnU22LHDOIaa/40e7bNdyKjqF0/rys6vQq5QrmB2D3pSu0/GoVRwd0pVieXFkSz724eKbu/JXNXdtZyuY2b0DvNZsZuXUXg4aPoGbNmplq02QypfuDvGQPz0L/ZGTYIIDBnJl7sVnsxo0bluF+D7zyyisMGzaMZs2asWHDBr799lu2bNmCra0tADNmzGDLli38+OOP6bbZo0cP8ubNy/Tp0y1lb7zxBt7e3vznP/95rDiDg4MBKLNnixYsFhEREfk3uBbBvk/aBZAfJjo6mpCQEMqWLfvUf5F/Fj1L/fMgNyhfvvwj61l11sFChQpRvHjxVC+AfPnyUaRIEdq2bcv9+/cZM2YMZ8+eZeXKlSxZsoQ+ffpY2rh37x4REf+bdrR79+5s2LCBxYsXc+7cOT788ENCQkLo2rVrmuOLiIiIiIg8CdlieveHyZcvH5999hkXLlygTZs2fPzxx4wYMYI2bdpY6kyaNInXX3/d8r5OnTpMnjyZb775hjZt2rBv3z4CAgLw9PS0ximIiIiIiMhzyOqzDv7VqVOnUr2vUKEC33333UPrT506NU3Zq6++yquvvprVoYmIiIiIiGRItku0sjNDqTKY8xe0dhgiIiIizzyDS97M1TcYMJlMGZ6oQP5dz2P/KNHKBLsGzawdgoiIiIikw2Qy4ePjY+0w5CH+Sf8kmc3YPoUJmhKtTJh06xKXErTwsYiIiIjIv6G4vQNj8he3dhiPRYlWJlxKiONMfIy1wxARERER+Uci9+zn/LS5RJ+7gE2OHBRs3oiSowdjmyMHp8dM4try1Rjs/pcqeL03lMKdXk+3ratfLuP3z74k/uYtTEWLUGLEW+Rv6Mft/Yf5rVvqRazNiYmY4xOo+etmjPnzcXL4OG5t/gljwQKUnjaOPNUrAxBz+QrHeg+hw8YVkP9/+3t4eHD9+nXs7FKnMVFRURiNRiIjIy3Tx8fGxpI/f35y587NlStXLMMWz58/j6enJ8HBwbzwwgsAzJ8/n4EDBzJz5kzeeeedf/bh/iFbzzooIiIiIiJZKz48guDugyjs3446v+2i2g/fcnvfQS4vWATA3d+OU3ryWPxO7LW8HpZkXf9+LRfn/BefOZOpe3wPxfr34Hi/ocTdCCPPi1VStVFr/1ZMxYviMXQADoUKErFzD3cOHMH3l40U6dKec5NmWto9M34anmPewdbBmOaYAQEB3L9/P83LYDCwe/duS72tW7fi4eHB/fv3+fXXXy3lW7ZswcPDw5JkQUqi1a9fP+bMmUNiYuI//oxBiZaIiIiIyHPFmC8vtQ5tx61dazAYSIi8Q3JcHPb58pIcF0/UqTPkrJCx56l+X/gFJYb2J1el8hgMBgq1bkqVlV9g6+ycpu6ZcVNxcC2Ix6BeABhsbVNtN9impCY3N23H1mQib92aGT4nJycn6tevz/bt2y1lq1evpkWLFjRp0oTVq1dbyrdu3UrLli0t77dt20ZYWBgzZswgOTmZ77//PsPHfRQlWiIiIiIizxk7ZycA9tZszMHGr2MsWAC3dq25H3IKc0IiF2Z+wi/VGvDrS624/MlizMnJadpIiokh6vQ5DLa2HGn/f+yuVI/Dr71JUnQMdk6Oqere3n+Ym+s3U3rK+5Yylzq+uNTxZf9Lrbm2bDVe7w8nKSaGizMX4PX+sEyfU/PmzS2JVnJyMuvWraN169a0atXKkmglJyezfft2WrVqZdlv3rx59OrVC5PJRP/+/ZkxY0amj50eg9lsNmdJS8+w4OBgAObld9AzWiIiIiLyzEiKjSXxzl1C3n4XGwcH3Hv6c3nBIjze7kOuqhW5f/wkx/oMwf3/OlOsT7dU+8Zdv8Fe38Y4epWk3IIPMXkUJ/SbFZyfNofqm77HVLSIpW5Qp944l/XGa+yjE6jzH87FPq8LLrVrcGbcVOxj4pjYfyB9+/YFUp7RCgsLw2hMPaTw6tWr3LhxA29vb8LDwwkODub1118nNDSUO3fuULBgQY4dO8a9e/do2LAht27dwt7enkuXLlG6dGnOnj2Lu7s7ERERuLu7s3HjRurVq5dujA9yg/Llyz/yXHRHS0RERETkOWWbIwcOhQpSctTbROz4hZwVylHpm4Xk8a2Gjb09uSqVx/3/OhO2fnOafQ1/JDvuPf1x8vbCxmiPe9c3yFHEjYif//esVMyl37m97yBFunV8ZCzR5y4SuXsfRbp15NTI8RTu3I7my5cwbtw4Tpw4Yam3YMECbt++nerl5OREyZIlKVWqFDt27GD16tW0bNkSGxsbXFxcqFu3Lhs3bmTLli00btwYe3t7S1sJCQlUqlSJ/Pnz4+3tTUJCAh999NE//myVaImIiIiIPEfuHAri1wavkhyfYClLjk/AYLQnctdeQpemfkYpOT4B2xwOadox5nXBPn9ezPHxqcrNScnwp0FzNzduJXe1SqnucKXnzPipeL0/HBs7O6JOnSNneR+MuXLi6enJ8ePHM3RuzZs3Z9euXWzYsIHWrVtbylu1asW2bdvYunWrZdhgbGwsgYGBBAYGEhQUZHmtW7eODRs2cPLkyQwd82GUaImIiIiIPEecyniTHBvL+WlzSI5PIPZKKOcmzcSt/asY7Ow5O/EjIn/5FbPZzJ1DR7m6+GvcHjLrYOFOr3Nx7qfcO36S5MREriz+mvgbYeR/5SVLnTsHg8j9YpVHxhS2bhMObq7krpYyvbupRDHuHAoiNiKS06dP4+XllaFza968OStXruT333/n5ZdftpS3bt2a/fv3s3//fpo2bQrA119/jcFgoHPnzri7u1teTZo0oXz58sycOfNhh8kQraMlIiIiIvIcsXNypMKS+Zz9z3T2VGuAbU5nCrVpjseg3tg4GEkYO4zTYycTd+0GxgL58XinL65tmgNY1sZ6cctKchRxw2NwX+xyOnNi0Ejirofh5FWC8os/xsG1kOV4MZevkLde7YfGk3g/ikvzP6Pi159aykr9ZzSnRozj4uRZjH57MJUrV87QudWpU4fw8HAaNWpEjhw5LOUlSpSgYMGCuLi4kC9fPiBlSvfOnTtbhhH+We/evRk2bBgffPABBQsWzNCx/0qTYWSAJsMQEREREfn3lTKa+NTN29phpKLJMERERERERKxEiZaIiIiIiEgW0zNamVDcPu1sKyIiIiIi8mQ8zd+/lWhlwpj8xa0dgoiIiIjIcyXJbMbWYLB2GJmmoYMZFB8fT0yMJsLIbmJiYjhx4oT6JhtS32Rv6p/sS32Tval/si/1Tfb2T/rnaUyyQIlWpmiCxuzHbDYTExOjvsmG1DfZm/on+1LfZG/qn+xLfZO9PY/9o0RLREREREQkiynREhERERERyWJKtERERERERLKYEi0REREREZEspkRLREREREQkiynREhERERERyWJKtERERERERLKYEi0REREREZEspkRLREREREQkiynREhERERERyWJKtERERERERLKYEi0REREREZEspkRLREREREQkiynREhERERERyWJKtERERERERLKYEi0REREREZEspkRLREREREQkixnMZrPZ2kFkd4cPH8ZsNmNvb4/BYLB2OPInZrOZhIQE9U02pL7J3tQ/2Zf6JntT/2Rf6pvs7Vnqn/j4eAwGA1WqVHlkPbt/KZ6n2oP/GZ72/ymeRQaDAaPRaO0wJB3qm+xN/ZN9qW+yN/VP9qW+yd6epf4xGAwZygt0R0tERERERCSL6RktERERERGRLKZES0REREREJIsp0RIREREREcliSrRERERERESymBItERERERGRLKZES0REREREJIsp0RIREREREcliSrRERERERESymBItERERERGRLKZES0REREREJIsp0RIREREREcliSrRERERERESymBKtR0hOTmbu3LnUrVuXihUr8n//939cunTJ2mEJcPXqVUqXLp3mtXz5cmuH9txbsGABXbp0SVUWEhKCv78/lSpVon79+gQGBlopuudben0zevToNNeRn5+flSJ8vty+fZv3338fPz8/qlSpQseOHTl48KBlu64b6/q7/tG1Yz3h4eEMHz4cX19fKleuTO/evTl79qxlu64d6/q7/nmerh07aweQnS1YsIBvv/2WKVOmUKhQIaZPn06vXr1Yv349RqPR2uE9106dOoWDgwNbt27FYDBYynPmzGnFqOTzzz9n7ty5VK9e3VIWGRlJ9+7defnll5kwYQJBQUFMmDCBPHny0LZtWytG+3xJr28g5Vrq27cv/v7+ljJbW9t/O7zn0pAhQwgPD2fmzJnkzZuXr7/+mh49erBy5Ury5s2r68bKHtU/np6eunasqF+/ftjY2LBw4UIcHR2ZM2cO3bp1Y8uWLcTGxurasbJH9Y/JZHq+rh2zpCsuLs5cuXJl89dff20pu3PnjrlChQrm9evXWzEyMZvN5k8++cTcqlUra4chf7h+/bq5R48e5kqVKpmbNGli9vf3t2wLCAgw161b15yQkGApmzFjhrlx48bWCPW586i+SUxMNJcvX968ZcsWK0b4fLp48aLZ29vbfOjQIUtZcnKyuVGjRubZs2frurGyv+sfXTvWExERYX7nnXfMp0+ftpSFhISYvb29zUePHtW1Y2V/1z/P27WjoYMPcfLkSaKiovD19bWU5cqVCx8fHw4cOGDFyARSfoX38vKydhjyh+PHj5M7d27Wrl1LxYoVU207ePAg1atXx87ufzfQfX19uXDhAuHh4f92qM+dR/XNxYsXiYuLw9PT00rRPb9cXFz49NNPeeGFFyxlBoMBs9nMnTt3dN1Y2d/1j64d63FxcWHmzJmUKlUKgFu3bhEYGIirqyteXl66dqzs7/rnebt2NHTwIa5fvw6Am5tbqvKCBQty7do1a4Qkf3L69GkKFChAp06duHjxIsWLF6d///7UrVvX2qE9lxo0aECDBg3S3Xb9+nW8vb1TlRUsWBCA0NBQ8uXL98Tje549qm9Onz6NwWBgyZIl7Ny5ExsbG+rVq8fgwYM1DPcJy5UrF/Xq1UtVtnHjRi5fvkydOnWYNWuWrhsr+rv+0bWTPYwdO5Zly5ZhNBr55JNPcHR01L852Uh6/fO8XTu6o/UQMTExAGmexXJwcCAuLs4aIckf4uPjuXjxIvfv32fw4MF8+umnlC9fnl69erF3715rhyd/ERsbm+51BOhasrIzZ85gY2NDkSJFCAgIYOTIkezYsYP+/fuTnJxs7fCeK4cOHeLdd9+lYcOGNGjQQNdNNvPX/tG1kz107dqVFStW0KpVKwYMGMDx48d1czDhzgAADnFJREFU7WQj6fXP83bt6I7WQ+TIkQNI+VL/4L8h5SI1mUzWCktISX4PHDiAnZ2d5S/TF154gXPnzhEYGEjNmjWtHKH8WY4cOYiPj09V9uAfO0dHR2uEJH8YNGgQ3bp1I1euXAB4e3tToEABOnToQHBwcJqhhvJkbN26lWHDhlGxYkVmzpwJ6LrJTtLrH1072cODRwgmTpxIUFAQX331la6dbCS9/pk8efJzde3ojtZDPBgyGBYWlqo8LCwMV1dXa4Qkf+Lo6JjmFytvb29u3LhhpYjkYVxdXdO9jgAKFSpkjZDkDwaDwfKP3QMPhtw8GD4tT9ZXX33FoEGD8PPzY+HChZYf9nTdZA8P6x9dO9YTHh7O+vXrSUpKspTZ2Njg6elp+Y6ma8d6/q5/nrdrR4nWQ5QpUwZnZ2d+/fVXS9ndu3c5ceIE1apVs2JkcvLkSSpXrpxqPROAY8eOaYKMbKh69eocOnQo1V+6e/fupUSJEhorb2VDhw6lR48eqcqCg4MBdC39C77++msmTpxI586dmT17dqofj3TdWN+j+kfXjvWEhYUxdOhQ9u/fbylLSEjgxIkTeHp66tqxsr/rn+ft2lGi9RBGoxF/f38++ugjtm3bxsmTJ3nnnXdwdXWlUaNG1g7vuebt7U2pUqWYMGECBw8e5Ny5c0yZMoWgoCD69u1r7fDkL9q2bcv9+/cZM2YMZ8+eZeXKlSxZsoQ+ffpYO7TnXosWLfjll1/45JNPuHz5Mjt27ODdd9+lRYsWz82MUNZy4cIFJk+eTKNGjejTpw/h/9/e3cdkVf9/HH9eIngD2bynxKlk2rgABYTLEANcDJPpsmVuKI6VNhPLSzJAS40pStslOOVWUzNQNNOadjttM7JU0JRutJVid3gT80JhCsrd9w/HyeuHitrVD5evx3Zt53zO55zzPp/PzuC9zzmfc/48lZWVVFZWUlNTo/umnbXVP7p32s9jjz1GWFiY8T/Azz//THJyMtXV1cTHx+veaWdt9c/9du+Ympubm9s7iHtVY2MjGRkZ7Nixg7q6OoKDg1m0aBFeXl7tHdp9z263Y7PZKC4uprq6Gh8fH+bNm6fRxntASkoKFRUVFBQUGGXfffcdaWlpHDt2jN69e/P88887fKhQ/n/cqG8+//xz8vLyKC8v54EHHmD8+PFYrVbj5XH5d+Tl5ZGZmXnDbRMnTiQ9PV33TTu6nf7RvdN+ampqWLFiBXv27KGmpoYRI0aQkpJiTCmue6d9tdU/99O9o0RLRERERETEyfTooIiIiIiIiJMp0RIREREREXEyJVoiIiIiIiJOpkRLRERERETEyZRoiYiIiIiIOJkSLRERERERESdToiUiIvclfd1ERET+TR3bOwAREbm3xcXFUVJS4lDm6upKr169iIyMxGq18uCDD7ZTdHcnNzcXV1dXpk+f3t6hGIYOHcrs2bN5+eWX2zsUERFxAiVaIiLSJh8fHxYvXmys19fX8+OPP5KRkcHx48cpKirCZDK1Y4R3ZuXKlcyePbu9wxARkf8wJVoiItImDw8Phg8f7lAWHBzMpUuXWLVqFWVlZa22i4iI3M/0jpaIiNw1X19fAE6fPm2U7dmzh2eeeQY/Pz9GjRrF0qVLuXz5srF99erVREVFkZWVhcVi4cknn6Sqqorm5mY2bdpETEwM/v7+REVFsXbtWod3qQ4dOsTUqVMZNmwYISEhJCcnY7fbje07duzAx8eHsrIyJk+ejJ+fHxEREaxdu9aoM3ToUACysrKM5Za4Y2NjCQgIwNfXl7Fjx1JYWOhwvSdPnmTGjBkEBgYSGhpKZmYm8+fPJy4uzqjT1NTEmjVriIqKwtfXl+joaAoKCv5pUwPQ2NjIpk2bGD9+PP7+/kRERGCz2bhy5QoAy5YtIyQkhKamJmOfhQsXMnToUMrLy42yzZs34+/vT21tLXD77bpt2zbCwsJ44okn+OWXX5xyTSIi/1VKtERE5K6dOnUKgP79+wOwa9cuEhIS8Pb2Jjs7m9mzZ7Nz505mzZrlkDCdPn2a3bt3k5GRgdVqpXv37mRkZJCWlkZ4eDi5ublMmjSJzMxMcnJyACgtLSU+Pp7OnTuzcuVKFixYQElJCdOmTaOurs44dlNTE1arlXHjxrFmzRqCgoKw2Wx89dVXAGzduhWAZ5991ljeu3cvCQkJmM1mcnJyWL16Nf369WPJkiV8++23ANjtdqZOncqZM2dYvnw5b7zxBp999hkfffSRQ5u8+eabrFq1igkTJpCXl8fYsWNZtmwZ2dnZ/7i9Fy1axLJlyxgzZgy5ublMmTKFwsJCo30jIyO5ePEiP/zwg7HPgQMHjPZrUVxczOOPP06XLl1uu10bGxvJy8tj6dKlWK1WBg8e/I+vR0Tkv0yPDoqISJuam5tpaGgw1i9evEhJSQm5ubkMHz4cX19fmpubsdlsjB49GpvNZtQdOHAg8fHxfPnll0RERADQ0NBAcnIyoaGhAFRXV7Nhwwbi4uJISkoCYNSoUdjtdg4fPgzAihUrGDRoEPn5+bi4uAAwbNgwYmJi2L59O1OmTDFinTVrFpMmTQIgKCiI3bt3s3fvXkaPHm084ujp6WksnzhxgqeffprXX3/diDsgIACLxUJpaSmBgYEUFBRw6dIlPvzwQ/r27WucPzo62tjn1KlTvPfeeyQmJvLiiy8CEBYWhslkIj8/n9jYWLp3735XfXDixAnef/99rFYrL730ktFGffr0ISkpieLiYkJDQ3F3d2f//v34+/tTUVHB77//jtlspqSkhMmTJ3P16lUOHjxIcnLyHbUrwMyZM40+FBGRW9OIloiItKm0tBSz2Wz8QkNDSUxMxGw2k5GRgclkory8nLNnzzJmzBgaGhqMX3BwMB4eHnz99dcOxxwyZIixfPToUerr64mKinKok5KSwvr166mtraWsrIzw8HAj6WtoaKB///488sgjrY4dEBBgLLu5udGjRw+Hxxf/r+nTp/PWW29x+fJlfvrpJz799FPWrFkDXJv4A66NDAUEBBhJFkC/fv0cznXgwAGam5tbtcGYMWO4cuWKkTTejZaZH8ePH+9QHhMTg4uLCwcPHsTV1ZVRo0bxzTffALB//34GDBjAU089ZexfWlrK5cuXiYyMvON2vb7PRETk1jSiJSIibTKbzaSmpgJgMpno1KkTDz30EB4eHkadCxcuAJCammrUvd5ff/3lsN6rV69W+/bo0eOG56+urqapqYm1a9c6vG/VolOnTg7rnTt3dljv0KHDLb+bZbfbWbx4MXv27MFkMjFgwACCgoKAv7+3ZbfbMZvNrfbt3bs3lZWVDtcRExNzw/OcO3fupjG05eLFi8b5rtexY0e6d+9OTU0NAOHh4aSmplJXV8f+/fuxWCxYLBZsNhu//vorxcXFmM1m+vbty7lz5+6oXXv27HnX8YuI3G+UaImISJvc3d3x8/O7ZZ1u3boBkJSUREhISKvtt/rWVsu+drsdb29vo/zMmTP89ttv+Pr6YjKZiI+Pv2ES06VLl9u6jpuZN28eJ0+eZMOGDQQGBuLm5kZtbS3btm0z6nh6enL+/PlW+15f1nIdGzduxN3dvVXdhx9++K5jbGm/yspKvLy8jPL6+nqqqqqMRxLDw8Opr6/n8OHDxiOCZrMZDw8PSktLKS4uZty4ccC1fv0321VE5H6mRwdFRMQpvL296dmzJ3/++Sd+fn7Gz9PTkxUrVnDs2LGb7uvv74+rqytffPGFQ/nGjRuZM2cOnTt3xsfHh/LycodjP/roo2RlZXHw4ME7irVDB8c/f4cPHyY6OpqRI0fi5uYGXJswAjBm8AsODubIkSPG6BVcS3qOHj1qrAcHBwNQVVXlEOeFCxdYuXKlMeJ1N1qS1127djmUf/zxxzQ2NhojcL1798bHx4eioiIqKysJCQnBxcWF4OBgPvjgA8rLy4mMjASuTdvvzHYVEZG/aURLREScwsXFhblz57Jo0SJcXFyIjIykurqanJwczp07d8PH7lr06NGDadOmsXHjRtzc3Bg5ciTff/89hYWFJCYm0rFjR2OCiVdffZUJEybQ2NjI+vXrKSsrMyaHuF3dunXjyJEjlJaWMmLECPz9/dm1axdmsxlPT0+OHDlCfn4+JpPJmAJ92rRpbNq0iRdeeIGEhAQAsrOzuXr1qvGx5iFDhjBhwgQWLlxIRUUFvr6+nDp1iszMTLy8vBg4cOAt4zp69CjvvPNOq/KwsDAGDx7MxIkTycrKoq6uDovFwvHjx41p8kePHm3Uj4iIIDs7m0GDBhnvlFksFtLT0+nTp49DXzizXUVE5G9KtERExGkmTZqEu7s7b7/9Nlu3bqVr164EBgZis9mMKeBv5rXXXqNXr14UFRWxfv16vLy8WLBgAbGxscC1ZGPdunVkZWXxyiuv4OrqitlsZsOGDXf8seSZM2eSk5PDjBkz+OSTT0hPT2fJkiUsWbIEuDZTYmpqKjt37uTQoUPAteTs3XffJS0tjaSkJNzd3YmNjaVr16507drVOPby5cvJz89ny5YtnD17lp49ezJu3DisVqsxq9/N7Nu3j3379rUqX758OYMHDyYtLY0BAwawfft21q1bR58+fYiLiyMhIcFhlK4l0br+EU6LxWJsa0kMnd2uIiLyN1Pzrd4OFhEREQDKysq4cOEC4eHhRllDQwMRERHExMQwf/78doxORETuNRrREhERuQ2nT59m7ty5JCQkEBISQm1tLVu2bKGmpobnnnuuvcMTEZF7jEa0REREblNRURGbN2/mjz/+wNXVlWHDhjFnzpw2Z2QUEZH7jxItERERERERJ9P07iIiIiIiIk6mREtERERERMTJlGiJiIiIiIg4mRItERERERERJ1OiJSIiIiIi4mRKtERERERERJxMiZaIiIiIiIiTKdESERERERFxMiVaIiIiIiIiTvY/d0+vc3CiMgoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "bar_width = 0.25\n",
    "\n",
    "index = np.arange(len(energies_df))\n",
    "\n",
    "bars3 = ax.barh(index, costs_df[\"Slowest meta heuristic is lower in %\"], color='lightgreen', height=bar_width, label=\"Worst Performing Metaheuristic\")\n",
    "bars1 = ax.barh(index + bar_width, energies_df[\"Average meta heuristic is lower in %\"], color='salmon', height=bar_width, label=\"Average Metaheuristic\")\n",
    "bars2 = ax.barh(index + 2*bar_width, energies_df[\"Fastest meta heuristic is lower in %\"], color='turquoise', height=bar_width, label=\"Best Performing Metaheuristic\")\n",
    "\n",
    "ax.set_yticks(index + bar_width)\n",
    "ax.set_yticklabels(energies_df[\"No of VMs\"]) \n",
    "ax.set_xlabel(\"Percentage Lower\")\n",
    "ax.set_ylabel(\"Number of VMs\")\n",
    "ax.set_title(\"Decrease in energy from the average of traditional allocation\")\n",
    "\n",
    "ax.legend()\n",
    "\n",
    "ax.invert_yaxis()\n",
    "\n",
    "for bar in bars1:\n",
    "    width = bar.get_width()\n",
    "    ax.text(width - 1, bar.get_y() + bar.get_height() / 2, f'{width:.2f}%', \n",
    "            ha='center', va='center', color='black', fontsize=10)\n",
    "\n",
    "for bar, algo in zip(bars2, energies_df[\"Best allocation\"]):\n",
    "    width = bar.get_width()\n",
    "    ax.text(width - 1, bar.get_y() + bar.get_height() / 2, f'{width:.2f}%', \n",
    "            ha='center', va='center', color='black', fontsize=10)\n",
    "    ax.text(width + 0.1, bar.get_y() + bar.get_height() / 2, algo, \n",
    "            ha='left', va='center', color='black', fontsize=10)\n",
    "\n",
    "for bar, algo in zip(bars3, energies_df[\"Worst allocation\"]):\n",
    "    width = bar.get_width()\n",
    "    ax.text(width - 1, bar.get_y() + bar.get_height() / 2, f'{width:.2f}%', \n",
    "            ha='center', va='center', color='black', fontsize=10)\n",
    "    ax.text(width + 0.1, bar.get_y() + bar.get_height() / 2, algo, \n",
    "            ha='left', va='center', color='black', fontsize=10)\n",
    "\n",
    "plt.savefig(\"energies_decrease_perc.png\", bbox_inches='tight') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1faca5a2-d42f-4c91-8295-9350afd5174e",
   "metadata": {},
   "source": [
    "# COST COMPARISON (VARYING TASK)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "id": "aed9ede7-39a4-4d8b-95a0-a8e881dd76ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cost_task=pd.read_excel(\"cost vs task 2.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "69451151-eaf6-4103-8787-d310b012a042",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No of Tasks</th>\n",
       "      <th>RR</th>\n",
       "      <th>SJF</th>\n",
       "      <th>FWA</th>\n",
       "      <th>SQSA</th>\n",
       "      <th>BAT</th>\n",
       "      <th>PSO</th>\n",
       "      <th>BMO</th>\n",
       "      <th>SSA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40</td>\n",
       "      <td>221.864169</td>\n",
       "      <td>222.761101</td>\n",
       "      <td>181.307046</td>\n",
       "      <td>172.153725</td>\n",
       "      <td>186.156727</td>\n",
       "      <td>198.402897</td>\n",
       "      <td>188.848631</td>\n",
       "      <td>183.965532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>270.011023</td>\n",
       "      <td>270.357121</td>\n",
       "      <td>208.241274</td>\n",
       "      <td>209.479435</td>\n",
       "      <td>220.202021</td>\n",
       "      <td>237.145815</td>\n",
       "      <td>229.112199</td>\n",
       "      <td>213.419658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>360.430813</td>\n",
       "      <td>360.938926</td>\n",
       "      <td>284.503415</td>\n",
       "      <td>287.227962</td>\n",
       "      <td>283.444481</td>\n",
       "      <td>318.860432</td>\n",
       "      <td>302.487126</td>\n",
       "      <td>290.436742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70</td>\n",
       "      <td>410.707769</td>\n",
       "      <td>411.856553</td>\n",
       "      <td>339.624416</td>\n",
       "      <td>334.002407</td>\n",
       "      <td>352.340518</td>\n",
       "      <td>373.893731</td>\n",
       "      <td>358.706109</td>\n",
       "      <td>337.592163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100</td>\n",
       "      <td>573.587505</td>\n",
       "      <td>573.233991</td>\n",
       "      <td>461.384704</td>\n",
       "      <td>483.394960</td>\n",
       "      <td>468.268521</td>\n",
       "      <td>520.994189</td>\n",
       "      <td>503.206992</td>\n",
       "      <td>490.611581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>150</td>\n",
       "      <td>773.910720</td>\n",
       "      <td>781.438072</td>\n",
       "      <td>669.669981</td>\n",
       "      <td>695.876084</td>\n",
       "      <td>675.685703</td>\n",
       "      <td>730.470104</td>\n",
       "      <td>712.962478</td>\n",
       "      <td>706.197953</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   No of Tasks          RR         SJF         FWA        SQSA         BAT  \\\n",
       "0           40  221.864169  222.761101  181.307046  172.153725  186.156727   \n",
       "1           50  270.011023  270.357121  208.241274  209.479435  220.202021   \n",
       "2           60  360.430813  360.938926  284.503415  287.227962  283.444481   \n",
       "3           70  410.707769  411.856553  339.624416  334.002407  352.340518   \n",
       "4          100  573.587505  573.233991  461.384704  483.394960  468.268521   \n",
       "5          150  773.910720  781.438072  669.669981  695.876084  675.685703   \n",
       "\n",
       "          PSO         BMO         SSA  \n",
       "0  198.402897  188.848631  183.965532  \n",
       "1  237.145815  229.112199  213.419658  \n",
       "2  318.860432  302.487126  290.436742  \n",
       "3  373.893731  358.706109  337.592163  \n",
       "4  520.994189  503.206992  490.611581  \n",
       "5  730.470104  712.962478  706.197953  "
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cost_task"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "6674747c-3e37-4d0d-a5d7-09b017ffaa8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "costs=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "996db727-9176-46e9-830c-7852606fadfd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of Tasks:  40\n",
      "[(172.153725, 'SQSA'), (181.307046, 'FWA'), (183.965532, 'SSA'), (186.156727, 'BAT'), (188.848631, 'BMO'), (198.402897, 'PSO'), (221.864169, 'RR'), (222.761101, 'SJF')]\n",
      "Average Traditional:  222.312635\n",
      "Average Metaheuristic:  185.139093\n",
      "Slowest meta heuristic is lower in %:  10.76\n",
      "Average meta heuristic is lower in %:  16.72\n",
      "Fastest meta heuristic is lower in %:  22.56\n",
      "Best allocation algorithm:  (172.153725, 'SQSA')\n",
      "Worst allocation algorithm:  (198.402897, 'PSO')\n",
      "\n",
      "No of Tasks:  50\n",
      "[(208.241274, 'FWA'), (209.479435, 'SQSA'), (213.419658, 'SSA'), (220.202021, 'BAT'), (229.112199, 'BMO'), (237.145815, 'PSO'), (270.011023, 'RR'), (270.357121, 'SJF')]\n",
      "Average Traditional:  270.184072\n",
      "Average Metaheuristic:  219.600067\n",
      "Slowest meta heuristic is lower in %:  12.23\n",
      "Average meta heuristic is lower in %:  18.72\n",
      "Fastest meta heuristic is lower in %:  22.93\n",
      "Best allocation algorithm:  (208.241274, 'FWA')\n",
      "Worst allocation algorithm:  (237.145815, 'PSO')\n",
      "\n",
      "No of Tasks:  60\n",
      "[(283.444481, 'BAT'), (284.503415, 'FWA'), (287.227962, 'SQSA'), (290.436742, 'SSA'), (302.487126, 'BMO'), (318.860432, 'PSO'), (360.430813, 'RR'), (360.938926, 'SJF')]\n",
      "Average Traditional:  360.6848695\n",
      "Average Metaheuristic:  294.49335966666666\n",
      "Slowest meta heuristic is lower in %:  11.6\n",
      "Average meta heuristic is lower in %:  18.35\n",
      "Fastest meta heuristic is lower in %:  21.41\n",
      "Best allocation algorithm:  (283.444481, 'BAT')\n",
      "Worst allocation algorithm:  (318.860432, 'PSO')\n",
      "\n",
      "No of Tasks:  70\n",
      "[(334.002407, 'SQSA'), (337.592163, 'SSA'), (339.624416, 'FWA'), (352.340518, 'BAT'), (358.706109, 'BMO'), (373.893731, 'PSO'), (410.707769, 'RR'), (411.856553, 'SJF')]\n",
      "Average Traditional:  411.282161\n",
      "Average Metaheuristic:  349.3598906666666\n",
      "Slowest meta heuristic is lower in %:  9.09\n",
      "Average meta heuristic is lower in %:  15.06\n",
      "Fastest meta heuristic is lower in %:  18.79\n",
      "Best allocation algorithm:  (334.002407, 'SQSA')\n",
      "Worst allocation algorithm:  (373.893731, 'PSO')\n",
      "\n",
      "No of Tasks:  100\n",
      "[(461.384704, 'FWA'), (468.268521, 'BAT'), (483.39496, 'SQSA'), (490.611581, 'SSA'), (503.206992, 'BMO'), (520.994189, 'PSO'), (573.233991, 'SJF'), (573.587505, 'RR')]\n",
      "Average Traditional:  573.410748\n",
      "Average Metaheuristic:  487.9768245\n",
      "Slowest meta heuristic is lower in %:  9.14\n",
      "Average meta heuristic is lower in %:  14.9\n",
      "Fastest meta heuristic is lower in %:  19.54\n",
      "Best allocation algorithm:  (461.384704, 'FWA')\n",
      "Worst allocation algorithm:  (520.994189, 'PSO')\n",
      "\n",
      "No of Tasks:  150\n",
      "[(669.669981, 'FWA'), (675.685703, 'BAT'), (695.876084, 'SQSA'), (706.197953, 'SSA'), (712.962478, 'BMO'), (730.470104, 'PSO'), (773.91072, 'RR'), (781.438072, 'SJF')]\n",
      "Average Traditional:  777.674396\n",
      "Average Metaheuristic:  698.4770505\n",
      "Slowest meta heuristic is lower in %:  6.07\n",
      "Average meta heuristic is lower in %:  10.18\n",
      "Fastest meta heuristic is lower in %:  13.89\n",
      "Best allocation algorithm:  (669.669981, 'FWA')\n",
      "Worst allocation algorithm:  (730.470104, 'PSO')\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(df_cost_task)):\n",
    "    row=df_cost_task.iloc[i]\n",
    "    row_tuple=[(row[col],col) for col in df_cost_task.columns]\n",
    "    \n",
    "    task=row_tuple[0][0]\n",
    "    print(\"No of Tasks: \",int(task))\n",
    "    \n",
    "    row_tuple=row_tuple[1:]\n",
    "    row_tuple.sort()\n",
    "    print(row_tuple)\n",
    "    \n",
    "    average_traditional=(row_tuple[-1][0]+row_tuple[-2][0])/2\n",
    "    print(\"Average Traditional: \",average_traditional)\n",
    "    \n",
    "    average_meta=0\n",
    "    for j in range(6):\n",
    "        average_meta+=row_tuple[j][0]\n",
    "    average_meta/=6\n",
    "    \n",
    "    print(\"Average Metaheuristic: \",average_meta)\n",
    "    \n",
    "    slowest_percentage_meta_lower=(average_traditional-row_tuple[-3][0])/average_traditional*100\n",
    "    print(\"Slowest meta heuristic is lower in %: \", round(slowest_percentage_meta_lower,2))\n",
    "    \n",
    "    average_percentage_meta_lower=(average_traditional-average_meta)/average_traditional*100\n",
    "    print(\"Average meta heuristic is lower in %: \", round(average_percentage_meta_lower,2))\n",
    "    \n",
    "    fastest_percentage_meta_lower=(average_traditional-row_tuple[0][0])/average_traditional*100\n",
    "    print(\"Fastest meta heuristic is lower in %: \", round(fastest_percentage_meta_lower,2))\n",
    "    \n",
    "    print(\"Best allocation algorithm: \", row_tuple[0])\n",
    "    print(\"Worst allocation algorithm: \", row_tuple[-3])\n",
    "    costs.append((task,average_traditional,average_meta,slowest_percentage_meta_lower,average_percentage_meta_lower,fastest_percentage_meta_lower,row_tuple[0][1],row_tuple[-3][1]))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "id": "d9e89bbc-1fba-4e1b-abf8-6542afa0c6e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No of Tasks</th>\n",
       "      <th>Average Traditional</th>\n",
       "      <th>Average Metaheuristic</th>\n",
       "      <th>Slowest meta heuristic is lower in %</th>\n",
       "      <th>Average meta heuristic is lower in %</th>\n",
       "      <th>Fastest meta heuristic is lower in %</th>\n",
       "      <th>Best allocation</th>\n",
       "      <th>Worst allocation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40.0</td>\n",
       "      <td>222.312635</td>\n",
       "      <td>185.139093</td>\n",
       "      <td>10.755006</td>\n",
       "      <td>16.721291</td>\n",
       "      <td>22.562330</td>\n",
       "      <td>SQSA</td>\n",
       "      <td>PSO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50.0</td>\n",
       "      <td>270.184072</td>\n",
       "      <td>219.600067</td>\n",
       "      <td>12.228055</td>\n",
       "      <td>18.722053</td>\n",
       "      <td>22.926147</td>\n",
       "      <td>FWA</td>\n",
       "      <td>PSO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60.0</td>\n",
       "      <td>360.684869</td>\n",
       "      <td>294.493360</td>\n",
       "      <td>11.595839</td>\n",
       "      <td>18.351618</td>\n",
       "      <td>21.414923</td>\n",
       "      <td>BAT</td>\n",
       "      <td>PSO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70.0</td>\n",
       "      <td>411.282161</td>\n",
       "      <td>349.359891</td>\n",
       "      <td>9.090701</td>\n",
       "      <td>15.055910</td>\n",
       "      <td>18.789960</td>\n",
       "      <td>SQSA</td>\n",
       "      <td>PSO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100.0</td>\n",
       "      <td>573.410748</td>\n",
       "      <td>487.976825</td>\n",
       "      <td>9.141189</td>\n",
       "      <td>14.899254</td>\n",
       "      <td>19.536788</td>\n",
       "      <td>FWA</td>\n",
       "      <td>PSO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>150.0</td>\n",
       "      <td>777.674396</td>\n",
       "      <td>698.477051</td>\n",
       "      <td>6.069930</td>\n",
       "      <td>10.183869</td>\n",
       "      <td>13.888128</td>\n",
       "      <td>FWA</td>\n",
       "      <td>PSO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   No of Tasks  Average Traditional  Average Metaheuristic  \\\n",
       "0         40.0           222.312635             185.139093   \n",
       "1         50.0           270.184072             219.600067   \n",
       "2         60.0           360.684869             294.493360   \n",
       "3         70.0           411.282161             349.359891   \n",
       "4        100.0           573.410748             487.976825   \n",
       "5        150.0           777.674396             698.477051   \n",
       "\n",
       "   Slowest meta heuristic is lower in %  Average meta heuristic is lower in %  \\\n",
       "0                             10.755006                             16.721291   \n",
       "1                             12.228055                             18.722053   \n",
       "2                             11.595839                             18.351618   \n",
       "3                              9.090701                             15.055910   \n",
       "4                              9.141189                             14.899254   \n",
       "5                              6.069930                             10.183869   \n",
       "\n",
       "   Fastest meta heuristic is lower in % Best allocation Worst allocation  \n",
       "0                             22.562330            SQSA              PSO  \n",
       "1                             22.926147             FWA              PSO  \n",
       "2                             21.414923             BAT              PSO  \n",
       "3                             18.789960            SQSA              PSO  \n",
       "4                             19.536788             FWA              PSO  \n",
       "5                             13.888128             FWA              PSO  "
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "costs_df=pd.DataFrame(costs,columns=[\"No of Tasks\",\"Average Traditional\",\"Average Metaheuristic\",\"Slowest meta heuristic is lower in %\",\n",
    "                            \"Average meta heuristic is lower in %\", \"Fastest meta heuristic is lower in %\", \n",
    "                            \"Best allocation\",\"Worst allocation\"])\n",
    "costs_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "36103256-3bf6-4320-bb3c-9946c454cab3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "bar_width = 0.25\n",
    "\n",
    "index = np.arange(len(costs_df))\n",
    "\n",
    "bars3 = ax.barh(index, costs_df[\"Slowest meta heuristic is lower in %\"], color='lightgreen', height=bar_width, label=\"Worst Performing Metaheuristic\")\n",
    "bars1 = ax.barh(index + bar_width, costs_df[\"Average meta heuristic is lower in %\"], color='salmon', height=bar_width, label=\"Average Metaheuristic\")\n",
    "bars2 = ax.barh(index + 2*bar_width, costs_df[\"Fastest meta heuristic is lower in %\"], color='turquoise', height=bar_width, label=\"Best Performing Metaheuristic\")\n",
    "\n",
    "ax.set_yticks(index + bar_width)\n",
    "ax.set_yticklabels(costs_df[\"No of Tasks\"]) \n",
    "ax.set_xlabel(\"Percentage Lower\")\n",
    "ax.set_ylabel(\"Number of Tasks\")\n",
    "ax.set_title(\"Decrease in cost from the average of traditional allocation\")\n",
    "\n",
    "ax.legend()\n",
    "\n",
    "ax.invert_yaxis()\n",
    "\n",
    "for bar in bars1:\n",
    "    width = bar.get_width()\n",
    "    ax.text(width - 1, bar.get_y() + bar.get_height() / 2, f'{width:.2f}%', \n",
    "            ha='center', va='center', color='black', fontsize=10)\n",
    "\n",
    "for bar, algo in zip(bars2, costs_df[\"Best allocation\"]):\n",
    "    width = bar.get_width()\n",
    "    ax.text(width - 1, bar.get_y() + bar.get_height() / 2, f'{width:.2f}%', \n",
    "            ha='center', va='center', color='black', fontsize=10)\n",
    "    ax.text(width + 0.1, bar.get_y() + bar.get_height() / 2, algo, \n",
    "            ha='left', va='center', color='black', fontsize=10)\n",
    "\n",
    "for bar, algo in zip(bars3, costs_df[\"Worst allocation\"]):\n",
    "    width = bar.get_width()\n",
    "    ax.text(width - 1, bar.get_y() + bar.get_height() / 2, f'{width:.2f}%', \n",
    "            ha='center', va='center', color='black', fontsize=10)\n",
    "    ax.text(width + 0.1, bar.get_y() + bar.get_height() / 2, algo, \n",
    "            ha='left', va='center', color='black', fontsize=10)\n",
    "\n",
    "plt.savefig(\"cost_decrease_perc_task.png\", bbox_inches='tight') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18923249-0457-45df-95c1-d4bd50114b64",
   "metadata": {},
   "source": [
    "# ENERGY COMPARISON (VARYING TASK)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "id": "ff514328-7e0e-42bc-8bb2-6c9ff3c0da71",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_energy_task=pd.read_excel(\"energy vs task 2.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "id": "8201d8d4-de1e-4847-be7b-fdd2be42a0e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No of Tasks</th>\n",
       "      <th>RR</th>\n",
       "      <th>SJF</th>\n",
       "      <th>FWA</th>\n",
       "      <th>SQSA</th>\n",
       "      <th>BAT</th>\n",
       "      <th>PSO</th>\n",
       "      <th>BMO</th>\n",
       "      <th>SSA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40</td>\n",
       "      <td>1.628516</td>\n",
       "      <td>1.654265</td>\n",
       "      <td>1.001985</td>\n",
       "      <td>0.954076</td>\n",
       "      <td>1.050232</td>\n",
       "      <td>1.283493</td>\n",
       "      <td>1.094436</td>\n",
       "      <td>1.004018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>1.793789</td>\n",
       "      <td>1.838367</td>\n",
       "      <td>1.316821</td>\n",
       "      <td>1.087307</td>\n",
       "      <td>1.395130</td>\n",
       "      <td>1.433944</td>\n",
       "      <td>1.291075</td>\n",
       "      <td>1.258292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>2.372618</td>\n",
       "      <td>2.380703</td>\n",
       "      <td>1.594865</td>\n",
       "      <td>1.506329</td>\n",
       "      <td>1.543471</td>\n",
       "      <td>1.879251</td>\n",
       "      <td>1.689366</td>\n",
       "      <td>1.588725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70</td>\n",
       "      <td>2.502110</td>\n",
       "      <td>2.494817</td>\n",
       "      <td>1.850538</td>\n",
       "      <td>1.594127</td>\n",
       "      <td>1.956910</td>\n",
       "      <td>2.071446</td>\n",
       "      <td>1.907841</td>\n",
       "      <td>1.707665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100</td>\n",
       "      <td>3.356497</td>\n",
       "      <td>3.432659</td>\n",
       "      <td>2.798496</td>\n",
       "      <td>2.672749</td>\n",
       "      <td>2.873112</td>\n",
       "      <td>2.974152</td>\n",
       "      <td>2.873852</td>\n",
       "      <td>2.795056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>150</td>\n",
       "      <td>3.528709</td>\n",
       "      <td>3.535136</td>\n",
       "      <td>3.264491</td>\n",
       "      <td>3.287681</td>\n",
       "      <td>3.396261</td>\n",
       "      <td>3.410273</td>\n",
       "      <td>3.313241</td>\n",
       "      <td>3.285107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   No of Tasks        RR       SJF       FWA      SQSA       BAT       PSO  \\\n",
       "0           40  1.628516  1.654265  1.001985  0.954076  1.050232  1.283493   \n",
       "1           50  1.793789  1.838367  1.316821  1.087307  1.395130  1.433944   \n",
       "2           60  2.372618  2.380703  1.594865  1.506329  1.543471  1.879251   \n",
       "3           70  2.502110  2.494817  1.850538  1.594127  1.956910  2.071446   \n",
       "4          100  3.356497  3.432659  2.798496  2.672749  2.873112  2.974152   \n",
       "5          150  3.528709  3.535136  3.264491  3.287681  3.396261  3.410273   \n",
       "\n",
       "        BMO       SSA  \n",
       "0  1.094436  1.004018  \n",
       "1  1.291075  1.258292  \n",
       "2  1.689366  1.588725  \n",
       "3  1.907841  1.707665  \n",
       "4  2.873852  2.795056  \n",
       "5  3.313241  3.285107  "
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_energy_task"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "id": "c150096f-da96-49b7-9bd5-9491a5f6e60c",
   "metadata": {},
   "outputs": [],
   "source": [
    "energies=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "id": "2d7b30e3-b4ee-4a54-9924-0fba87b59c08",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of Tasks:  40\n",
      "[(0.954076, 'SQSA'), (1.001985, 'FWA'), (1.004018, 'SSA'), (1.050232, 'BAT'), (1.094436, 'BMO'), (1.283493, 'PSO'), (1.628516, 'RR'), (1.654265, 'SJF')]\n",
      "Average Traditional:  1.6413905\n",
      "Average Metaheuristic:  1.0647066666666667\n",
      "Slowest meta heuristic is lower in %:  21.8\n",
      "Average meta heuristic is lower in %:  35.13\n",
      "Fastest meta heuristic is lower in %:  41.87\n",
      "Best allocation algorithm:  (0.954076, 'SQSA')\n",
      "Worst allocation algorithm:  (1.283493, 'PSO')\n",
      "\n",
      "No of Tasks:  50\n",
      "[(1.087307, 'SQSA'), (1.258292, 'SSA'), (1.291075, 'BMO'), (1.316821, 'FWA'), (1.39513, 'BAT'), (1.433944, 'PSO'), (1.793789, 'RR'), (1.838367, 'SJF')]\n",
      "Average Traditional:  1.816078\n",
      "Average Metaheuristic:  1.2970948333333334\n",
      "Slowest meta heuristic is lower in %:  21.04\n",
      "Average meta heuristic is lower in %:  28.58\n",
      "Fastest meta heuristic is lower in %:  40.13\n",
      "Best allocation algorithm:  (1.087307, 'SQSA')\n",
      "Worst allocation algorithm:  (1.433944, 'PSO')\n",
      "\n",
      "No of Tasks:  60\n",
      "[(1.506329, 'SQSA'), (1.543471, 'BAT'), (1.588725, 'SSA'), (1.594865, 'FWA'), (1.689366, 'BMO'), (1.879251, 'PSO'), (2.372618, 'RR'), (2.380703, 'SJF')]\n",
      "Average Traditional:  2.3766605\n",
      "Average Metaheuristic:  1.6336678333333332\n",
      "Slowest meta heuristic is lower in %:  20.93\n",
      "Average meta heuristic is lower in %:  31.26\n",
      "Fastest meta heuristic is lower in %:  36.62\n",
      "Best allocation algorithm:  (1.506329, 'SQSA')\n",
      "Worst allocation algorithm:  (1.879251, 'PSO')\n",
      "\n",
      "No of Tasks:  70\n",
      "[(1.594127, 'SQSA'), (1.707665, 'SSA'), (1.850538, 'FWA'), (1.907841, 'BMO'), (1.95691, 'BAT'), (2.071446, 'PSO'), (2.494817, 'SJF'), (2.50211, 'RR')]\n",
      "Average Traditional:  2.4984634999999997\n",
      "Average Metaheuristic:  1.8480878333333335\n",
      "Slowest meta heuristic is lower in %:  17.09\n",
      "Average meta heuristic is lower in %:  26.03\n",
      "Fastest meta heuristic is lower in %:  36.2\n",
      "Best allocation algorithm:  (1.594127, 'SQSA')\n",
      "Worst allocation algorithm:  (2.071446, 'PSO')\n",
      "\n",
      "No of Tasks:  100\n",
      "[(2.672749, 'SQSA'), (2.795056, 'SSA'), (2.798496, 'FWA'), (2.873112, 'BAT'), (2.873852, 'BMO'), (2.974152, 'PSO'), (3.356497, 'RR'), (3.432659, 'SJF')]\n",
      "Average Traditional:  3.394578\n",
      "Average Metaheuristic:  2.8312361666666668\n",
      "Slowest meta heuristic is lower in %:  12.39\n",
      "Average meta heuristic is lower in %:  16.6\n",
      "Fastest meta heuristic is lower in %:  21.26\n",
      "Best allocation algorithm:  (2.672749, 'SQSA')\n",
      "Worst allocation algorithm:  (2.974152, 'PSO')\n",
      "\n",
      "No of Tasks:  150\n",
      "[(3.264491, 'FWA'), (3.285107, 'SSA'), (3.287681, 'SQSA'), (3.313241, 'BMO'), (3.396261, 'BAT'), (3.410273, 'PSO'), (3.528709, 'RR'), (3.535136, 'SJF')]\n",
      "Average Traditional:  3.5319225000000003\n",
      "Average Metaheuristic:  3.3261756666666664\n",
      "Slowest meta heuristic is lower in %:  3.44\n",
      "Average meta heuristic is lower in %:  5.83\n",
      "Fastest meta heuristic is lower in %:  7.57\n",
      "Best allocation algorithm:  (3.264491, 'FWA')\n",
      "Worst allocation algorithm:  (3.410273, 'PSO')\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(df_energy_task)):\n",
    "    row=df_energy_task.iloc[i]\n",
    "    row_tuple=[(row[col],col) for col in df_energy_task.columns]\n",
    "    \n",
    "    task=row_tuple[0][0]\n",
    "    print(\"No of Tasks: \",int(task))\n",
    "    \n",
    "    row_tuple=row_tuple[1:]\n",
    "    row_tuple.sort()\n",
    "    print(row_tuple)\n",
    "    \n",
    "    average_traditional=(row_tuple[-1][0]+row_tuple[-2][0])/2\n",
    "    print(\"Average Traditional: \",average_traditional)\n",
    "    \n",
    "    average_meta=0\n",
    "    for j in range(6):\n",
    "        average_meta+=row_tuple[j][0]\n",
    "    average_meta/=6\n",
    "    \n",
    "    print(\"Average Metaheuristic: \",average_meta)\n",
    "    \n",
    "    slowest_percentage_meta_lower=(average_traditional-row_tuple[-3][0])/average_traditional*100\n",
    "    print(\"Slowest meta heuristic is lower in %: \", round(slowest_percentage_meta_lower,2))\n",
    "    \n",
    "    average_percentage_meta_lower=(average_traditional-average_meta)/average_traditional*100\n",
    "    print(\"Average meta heuristic is lower in %: \", round(average_percentage_meta_lower,2))\n",
    "    \n",
    "    fastest_percentage_meta_lower=(average_traditional-row_tuple[0][0])/average_traditional*100\n",
    "    print(\"Fastest meta heuristic is lower in %: \", round(fastest_percentage_meta_lower,2))\n",
    "    \n",
    "    print(\"Best allocation algorithm: \", row_tuple[0])\n",
    "    print(\"Worst allocation algorithm: \", row_tuple[-3])\n",
    "    energies.append((task,average_traditional,average_meta,slowest_percentage_meta_lower,average_percentage_meta_lower,fastest_percentage_meta_lower,row_tuple[0][1],row_tuple[-3][1]))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "id": "a09f9cb4-36e5-417c-8fd7-5b2b3940b510",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No of Tasks</th>\n",
       "      <th>Average Traditional</th>\n",
       "      <th>Average Metaheuristic</th>\n",
       "      <th>Slowest meta heuristic is lower in %</th>\n",
       "      <th>Average meta heuristic is lower in %</th>\n",
       "      <th>Fastest meta heuristic is lower in %</th>\n",
       "      <th>Best allocation</th>\n",
       "      <th>Worst allocation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40.0</td>\n",
       "      <td>1.641390</td>\n",
       "      <td>1.064707</td>\n",
       "      <td>21.804531</td>\n",
       "      <td>35.133860</td>\n",
       "      <td>41.873917</td>\n",
       "      <td>SQSA</td>\n",
       "      <td>PSO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50.0</td>\n",
       "      <td>1.816078</td>\n",
       "      <td>1.297095</td>\n",
       "      <td>21.041717</td>\n",
       "      <td>28.577141</td>\n",
       "      <td>40.128838</td>\n",
       "      <td>SQSA</td>\n",
       "      <td>PSO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60.0</td>\n",
       "      <td>2.376660</td>\n",
       "      <td>1.633668</td>\n",
       "      <td>20.928925</td>\n",
       "      <td>31.262045</td>\n",
       "      <td>36.619934</td>\n",
       "      <td>SQSA</td>\n",
       "      <td>PSO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70.0</td>\n",
       "      <td>2.498463</td>\n",
       "      <td>1.848088</td>\n",
       "      <td>17.091204</td>\n",
       "      <td>26.031025</td>\n",
       "      <td>36.195706</td>\n",
       "      <td>SQSA</td>\n",
       "      <td>PSO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100.0</td>\n",
       "      <td>3.394578</td>\n",
       "      <td>2.831236</td>\n",
       "      <td>12.385221</td>\n",
       "      <td>16.595342</td>\n",
       "      <td>21.264175</td>\n",
       "      <td>SQSA</td>\n",
       "      <td>PSO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>150.0</td>\n",
       "      <td>3.531923</td>\n",
       "      <td>3.326176</td>\n",
       "      <td>3.444286</td>\n",
       "      <td>5.825350</td>\n",
       "      <td>7.571839</td>\n",
       "      <td>FWA</td>\n",
       "      <td>PSO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   No of Tasks  Average Traditional  Average Metaheuristic  \\\n",
       "0         40.0             1.641390               1.064707   \n",
       "1         50.0             1.816078               1.297095   \n",
       "2         60.0             2.376660               1.633668   \n",
       "3         70.0             2.498463               1.848088   \n",
       "4        100.0             3.394578               2.831236   \n",
       "5        150.0             3.531923               3.326176   \n",
       "\n",
       "   Slowest meta heuristic is lower in %  Average meta heuristic is lower in %  \\\n",
       "0                             21.804531                             35.133860   \n",
       "1                             21.041717                             28.577141   \n",
       "2                             20.928925                             31.262045   \n",
       "3                             17.091204                             26.031025   \n",
       "4                             12.385221                             16.595342   \n",
       "5                              3.444286                              5.825350   \n",
       "\n",
       "   Fastest meta heuristic is lower in % Best allocation Worst allocation  \n",
       "0                             41.873917            SQSA              PSO  \n",
       "1                             40.128838            SQSA              PSO  \n",
       "2                             36.619934            SQSA              PSO  \n",
       "3                             36.195706            SQSA              PSO  \n",
       "4                             21.264175            SQSA              PSO  \n",
       "5                              7.571839             FWA              PSO  "
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "energies_df=pd.DataFrame(energies,columns=[\"No of Tasks\",\"Average Traditional\",\"Average Metaheuristic\",\"Slowest meta heuristic is lower in %\",\n",
    "                            \"Average meta heuristic is lower in %\", \"Fastest meta heuristic is lower in %\", \n",
    "                            \"Best allocation\",\"Worst allocation\"])\n",
    "energies_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "id": "c1964fb1-1c0b-4899-b8d8-acab71d77ccd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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A9u3bKVy4MNmyZbOc387OjmrVqrFixQqrc/777wlI1t/Kjh07KFmyJNmyZbPUy5kzp9V4P+nfy65du8iZMyelS5e22t+kSRN+/fVXDh48yKuvvpqoXko82OfXX3+d119/nejoaP755x/Onz/PkSNHiI+Pt/R5z549ODo6Uq1aNUs9V1dXXn31VXbv3v1EcTzN6/nB94zChQszf/58EhISuHDhAufOnePkyZOcOXPmsdNn/y05v/9HOXDgADExMTRu3Nhqf5kyZciZMyc7d+6kbdu27N27l1dffRVnZ2dLmWLFirFx40bLdpUqVYiNjeXs2bOcO3eO48ePc/PmTcvf7eOk5viKPO+UmIm8xK5evYqLiwseHh7cunULwHKP0oOuXbvG7du3MQyDTJkyPbbtf5e53/aoUaMYNWpUkm3Dvft2Ro4cyfr16zGZTOTJk8fywc/4/2fgNGjQgISEBObPn8/EiRP59ttvyZkzJwMGDKBhw4aWczVs2PChfU4JFxcXq207O7tHPo/n1q1b/Pbbb/z222+Jjj14s/uj2k5JP+4nM/eFhoYm+Tt6sFyDBg0YM2YMK1asoFu3bgQGBtKpU6eH9OzRkvp9P3i++/v+fQ8V3EsaH/Tg2DypB1cBTGqMH/Waf5gVK1bwzTffcOXKFTw8PChUqFCimOvVq8cnn3xiGddVq1ZRp04dS0y3bt3i/PnzFClSJMlz/Htp+QfHMjl/Kzdv3kyy7SxZsljucXvSv5fbt28/9PcLT5ZEP6yt+6Kiovjkk09Yvnw5cXFxeHt7U7JkSRwcHCx9vn37Nh4eHtjZWd9CnyVLlieOI6Wv58e9Z8ycOZOpU6cSGhpK5syZKVKkCGazOdH9no+SnN//o9y/j+xhfbofy61btx75fp+QkMA333zDvHnziIiIwMvLi2LFilklco+T2uMr8jxTYibykoqPj2fXrl2UKlUKe3t70qdPD8BXX32Fj49PovKZM2fG3d0dk8mUaEGDmJgYtm/fTrFixZI81/22Bw8ebLVk8n0ZMmQAYODAgZw+fZqZM2dSqlQpnJyciIyMZNGiRVblGzVqRKNGjbh79y5bt25l2rRpDBo0iDJlyljONWvWLNzc3BKdK0eOHI8ZmaeTLl06KlWqRJcuXRIdc3BI/lvu0/QjW7ZsnD9/PtH+GzduWG27ublRr149AgMDKVy4MHfu3OH1119PdowPc/+b8uvXryc6FhISgqen51OfIzUk5zWflD179jBkyBDat29P165dyZ49OwBffPGF5Woc3Es4a9WqRWBgIFWqVOHYsWMMGTLEcjxdunSUK1eOwYMHJ3keJyenh8aenL+V7NmzJ/qdg/Xr4ElfZxkyZEjyNXY/4UuL3/Ho0aNZu3Yt48ePp1KlSparxBUrVrSU8fT0JDQ0lPj4eOzt7S3773/4fxKp+XpeuXIln332GQMGDKBly5aWL2vee+89yxWv5Ejue+XD3H/PvX79Ovnz57c6FhISQq5cuYB7r9EH3+8BNm/eTKFChVi6dCk//fQTAQEB1K1bl3Tp0gHQsmXLZPfleXm/EPkvaFVGkZfUzz//zLVr1ywPdi1evDiOjo5cvXoVf39/y4+joyNff/01Fy9exM3NjcKFC7NhwwartrZu3UqPHj0IDg5O8lz58uUjU6ZMXLx40art7Nmz8/XXX1u+Ed27dy9169alQoUKlg+lW7ZsAbCsqtavXz/69OkD3PvQUL9+fXr16kV8fDzXrl2jbNmywL2rRv8+161btxg/fvxTfUBLjnLlynHq1CkKFy5sOXfRokX56aef+P3335PdztP0o1y5cly4cIGgoCDLvujoaMtY/lvLli05ceIEM2bMoEKFCqmSuObNm5csWbKwcuVKq/0XLlzgwIEDlCpV6qnP8aAHr5AkR3Je80nZv38/CQkJvPvuu5akLD4+3jJV9d8rADZt2pSDBw8yb948smbNSoUKFSzHypUrx9mzZ8mbN6/V+VesWMGiRYusEosHJedvpWzZsuzfv9+SLMG9D7oHDhywbD/p66xs2bJcunTJKhGFe1cSHR0dH/olTVKS+7vbu3cv5cuX57XXXrMkZYcPH+bmzZuWPlesWJG4uDjWr19vqRcTE8Nff/31xDGk5ut57969pEuXjh49eliSsvDwcPbu3Zto5cjHtfO43/+jFC9eHCcnp0R92rNnD5cvX7b0qUyZMvz555/ExMRYyhw/fpwePXpw6NAh9u7dS4ECBWjZsqUlKbt69SonTpywiuO/Gl+R552umIm84MLCwiwfxBISEggNDWXr1q388ssvNGnShDp16gD3vmnu1q0b3377LWFhYZQvX56rV6/y7bffYjKZKFSoEADvvvsuPXv2pF+/fjRv3pybN2/y9ddfU6NGDQoXLmyVDNxnb2/P+++/z0cffYS9vT01atTgzp07TJ48matXr1qmWxUrVoyVK1dSpEgRsmfPzv79+5k6dSomk8kyratChQqMHDmSzz//nGrVqnHnzh0mTpyIj48PhQoVwtHRkSZNmjBixAguXbpE0aJFOXv2LOPGjcPb2zvJKyOpqVevXrz55pu8/fbbtGnTBmdnZ3755RfWr1/Pd999l+x2fH19n7gfjRo14ocffqB379689957pE+fnhkzZnDjxo1EiVfp0qXJly8fu3bt4quvvnrSbluxs7Ojf//+DBs2jPfff5/XX3+d0NBQJk6cSIYMGZK8mvi00qdPz/Xr19m8eTOFCxdOVp3kvuYfdD/p+Pjjj2nRogV37txh7ty5HDt2DLh338/96ZlVqlQhY8aM/Pzzz3Tu3NnqA2rnzp1Zvnw5nTt35q233sLT05PffvuNhQsXMmzYsEfGnpy/lY4dOzJv3jy6du1K7969AZg0aRIxMTGWezKf9HXWvHlz5s+fT58+fXj33XfJlSsXGzduZPHixfTp08dyJS457pddtWoVxYsXt1ytSarPgYGBLFiwgPz583Ps2DG+//57qz5XrFiRKlWq8OGHH3Ljxg1y5szJ7NmzuXnz5iOn5KVPn56jR4+ya9euREllar6eixUrxoIFC/jss8+oUaMG165dY/r06Vy/ft1yFSu57Tzu9/8oHh4e9OjRg4kTJ+Lo6EitWrW4ePEi3377LQUKFKB58+bAvfezN954g+7du9OpUydiYmL49ttvKVKkCNWqVSMoKIjJkyfzww8/UKJECc6fP8/UqVOJiYmxiuO/Gl+R550SM5EX3NGjR3njjTeAe/8AZsqUibx58/LZZ58luvG7X79+ZMmShfnz5/Pjjz+SIUMGKlasSP/+/S3fhtaoUYOpU6cyYcIEevfujaenJ/Xr1+e99957ZBytWrXCzc2NH3/8kV9++QVXV1dKlSrFV199Zfkg9tlnn/HJJ5/wySefAODj48OoUaNYsWIFe/bsAeDNN98kNjaWn3/+mfnz5+Pi4kLFihUZNGgQjo6OAIwdO5apU6fy888/ExwcTKZMmWjQoAH9+vV75FWI1FCoUCHmzZvHuHHjGDx4MIZh4Ovry6RJk6hVq1aK2nrSfjg4ODB9+nRGjx5NQEAADg4ONGnSBE9PT86ePZuofPXq1QkJCbEsSpEamjdvjpubG1OnTqV37964u7tTtWpV+vfv/1T3+zzqfJs3b6Z37968++67yX6gcXJe8w8qX748H330ETNnzmTNmjVkzpyZ8uXLM3HiRHr37m1ZMAHufSnRsGFDZs2alehB7tmyZePnn3/m66+/JiAggOjoaHx8fBg9evRjp4Il528lffr0zJ49m9GjRzN48GDc3Nxo27Ytrq6uVovFPMnrzGw2M2fOHL7++mu+++47wsLCyJcvX7Jif1CdOnVYvnw5Q4cOpWXLlgQEBCRZbujQocTGxjJ+/HhiYmLw9vamZ8+enDp1io0bN1qmL06cOJGvvvqK7777jujoaBo0aEDr1q0TXen/t7feeosxY8bQtWtXZs6cmeh4ar2emzVrxsWLF1m8eDHz588nW7ZsvPrqq7Rt25YRI0Zw6tSpxz78GpL3+3+cvn37kjlzZubOncuiRYvw8PCgXr169OvXz3IfpJ+fn+X3/P777+Pm5sarr77KwIEDcXJy4u233yY0NJTZs2czadIkvLy8aNq0KSaTialTp3L79m0yZMjwn42vyPPOZOiOSRGRF8r9Vd7q1KljuTIC91Zh9PLysjzQF+4tFNC4cWPKly/PiBEjbBGupJGDBw9y69Ytq9UR4+LiqF69Og0bNnzsVTkREflv6YqZiMgLJiIigvfee4+2bdtSu3Zt4uPjWbVqFUeOHGHQoEHAvSmuP/30E4cOHeLcuXNMnjzZxlFLart8+TLvv/8+vXv3ply5ckRGRvLzzz9z9+5dWrdubevwRETkAbpiJiLyAlqzZg3Tp0/n9OnTGIaBn58fPXv2pEqVKsD/rpwkJCQwZMgQmjZtauOIJS0sWLCA+fPnc+HCBRwdHSlevDjvvfce/v7+tg5NREQeoMRMRERERETExrRcvoiIiIiIiI0pMRMREREREbExJWYiIiIiIiI2plUZU8H+/fsxDMPyDCUREREREXk5xcbGYjKZKFmyZIrq6YpZKjAMw/Ijqc8wDGJiYjS+aUTjm7Y0vmlPY5y2NL5pS+ObtjS+aUvjm7QnzQt0xSwVODo6EhMTQ4ECBXB1dbV1OC+ciIgIgoKCNL5pROObtjS+aU9jnLY0vmlL45u2NL5pS+ObtEOHDj1RPV0xExERERERsTElZiIiIiIiIjamxExERERERMTGlJiJiIiIiIjYmBIzERERERERG1NiJiIiIiIiYmNKzERERERERGxMiZmIiIiIiIiNKTETERERERGxMSVmIiIiIiIiNqbETERERERExMaUmImIiIiIiNiYEjMREREREREbU2ImIiIiIiJiY0rMREREREREbEyJmYiIiIiIiI0pMRMREREREbExJWYiIiIiIiI2psQsFZlMJluH8EIymUyYzWaNbxrR+KYtjW/a0xiLiMiLwMHWAbwonJycMJvNtg7jhWQ2m/Hz87N1GC8sjW/a0vimPVuPcYKRgJ1J33OKiMjTUWKWitaEr+Fm/E1bhyEiIv+RjPYZqedWz9ZhiIjIC0CJWSq6GX+TkPgQW4chIiIiIiLPGc29EBERERERsTElZiIiYlOXDl9icrPJfJDvA0YUGsHcnnMJuxFmVebsrrMM9Br4yHZiImNY2H8hIwqNYFjeYUx6fRKXj1y2HI8Oj2Z+7/l8kP8DhuYZytyec4kOiwYgIT6BuT3nMiT3EMaUH8OZHWcs9a6fu84XVb8gLjrusX3x8fHBxcUFd3d30qVLh5ubGzly5GDQoEEkJCQAMHPmTEqVKkW6dOnIkCEDVatWZfXq1VbtGIbBDz/8QNmyZXF3d8fT05PKlSsze/bsx8YgIiLPJyVmIiJiMzGRMUxtPZW85fLy8bGPGbptKBE3I1jQZwFwL0HZMXcHU1pOeWxitObzNYScDmHo9qF8cvwTchTJwfQO0y3HFw9ezK3Ltxi+ZzjD9wwn9GIoK0etBODYxmOc3XGWkYdGUvmtyiwfsdxSb8nQJTT9uCkOzsmb/T9lyhTCwsK4e/cu4eHhrF27llmzZjFq1Cjmz5/PsGHDmDx5Mrdv3yYkJITu3bvTvHlztmzZYmmjXbt2jB07luHDh3P16lWuXbvGkCFDGD58OF27dk32+IqIyPND95iJiIjNhF4MJUeRHNQdXBc7ezscMjpQqXMl5r4zF4AFfRZw9eRV6g2pZ5UsJeXqiav3rkoZgAF29nY4mZ0AiImIYe+ve+mzog9unm4ANB7ZmElNJ9FkVBPs7P//e0rj3n/ub/+9+m+cXJ0oWKPgE/fR39+fatWqsW/fPkJCQihevDgVKlQA7q3o27FjR86fP09oaCgAy5cvZ/HixQQFBZEvXz5LO02aNKFAgQIUK1aMli1bUr9+/SeOSUREnj1KzERExGayvZKNdxa9Y7XvwIoD5CqRC4AGHzTAI6cHJ7eefGxbNXrXYGanmQwvMBw7ezvcMrnRe3lvAELOhBAfG4+Xn5elfPaC2YmNjOXa6Wv4VvfFt7ovo8uOJoNXBt4c/yYxETEEjgnknV/fedgpHys2Npa//vqLjRs3MmrUKIoUKULdunWpV68ejRo1okKFChQvXpwRI0ZY6ixdupTKlStbJWX3+fn5UalSJRYtWqTETETkBaPETEREngmGYfDbmN84suYIfVf3BcAjp0ey6yfEJVCscTHqDqqLSzoXVoxcwfT20xn852DLvWRObk6W8k6u/381LTwGOzs73hj3Bm+Me8NyfNUnqyjfrjzhN8OZ1W0WMRExVOxYkcpdKj8yjl69etGvXz/Ltre3NwMGDKBPnz6YTCb27t3L5MmTGT9+PKdPn8bd3Z327dvz5Zdf4u7uzuXLl8mePftD28+RIweXL19+6HEREXk+KTETERGbi7oTxfw+87lw8AJ9V/clh1+OFNWPj43npy4/0eOXHnjk8ACgxectGJZ3GMf/OG7ZFxsRi7O7M3BveiNg2f63qyevcvyP4/Rb24/xdcdTvVd1/Gr7MbrsaPJXzE/2Qg9PnCZPnkznzp0ferxYsWJMmTIFgJCQENavX8/gwYMJCwtjzpw5eHl5cerUqYfWP3v2rB5aLiLyAtLiHyIiYlPXz17n69e+JupuFAM2DkhxUgb3VlyMuBVBXMz/Fggx2Zsw2Zmwd7Qna4Gs2Dvac+XYFcvx4OPB2DvZkyV/lkTtLRmyhGajm2HvYE9wUDC5iufCnN5MZp/MBB8LfrKOArlz52bSpEmW7SxZstCmTRsGDx7M/v37AWjVqhW7d+/m0KFDierv37+fffv20aJFiyeOQUREnk1KzERExGYibkUwqekk8pbNyzuL38E9k/sTtePq4Uq+CvlYGbCSuyF3iY2KZWXAStwyupGvQj6cXJ0o2awkqz5eRdj1MMKuh7Hq41WUalHKskDIffuW7MMjpwf5Kty7xytzvsyc3XWW8JvhhJwOIXO+zE/c3/bt2/Ppp5+yatUqbt++TVxcHAcPHmT69OmWZKtRo0a0a9eOxo0bs2LFCsLDwwkPD2f58uU0bdqUNm3a0KhRoyeOQUREnk2ayigiIjazc95OQi+GcmD5AQ6sOGB17IsLXzyy7untp5naeirDtg/D09uTLj91YfnI5XxR9QviY+PxKePDO7++g7PbvamKLb9syfIRy/m8yufEx8RTtH5RWnxhfeUp6m4Uv3/zu2XRkPv1FvRdwIqPVlC1e1W8i3k/cX9Hjx5Njhw5CAgI4Pjx4yQkJJA3b166devGe++9Zyk3c+ZMZsyYwdixY+nYsSNwb+GPUaNGPXKapIiIPL9MhmEYtg7ieXd/usmhPIcIiQ+xcTQiIvJfyWKfhbbp29o6jDQVERFBUFAQhQsXxtXV1dbhvHA0vmlL45u2NL5Ju58b+Pv7p6iepjKKiIiIiIjYmBIzERERERERG1NiJiIiIiIiYmNa/CMV+Tj64GnnaeswRETkP5LBPoOtQ0hzJpMJs9mMyWSydSgvJI1v2tL4yvNEiVkqqmSuZOsQREREUpXZbNYDrdOQxjdt/Xt8jYQETHaaLCbPLiVmqShuyTyMkKu2DkNERERE/sWUJRsOzdvZOgyRR1JiloqMkKsQfMnWYYiIiIjIv+jZUPI80PVcERERERERG3uuErOzZ89SsmRJlixZYtkXFBRE+/btKVGiBNWrV2f69OmPbScwMJAGDRrg7+9P48aN2bJlS1qGLSIiIvLc23TmHypPm0+mMRPJ9eUU+v22kcjYWAD6rFqP+yff4jl6guXnxz1/P7bN/oGb6Lp0jdW+A1euUeenRWQeOxGvzyfTeUkgNyIiAYhPSKDLkkAyjZlI0Qkz+ev8/2Yqnbl5i9LfzyY6Li4Vey3y33luErPY2FgGDhxIRESEZV9oaChdunTBx8eHxYsX07dvX7799lsWL1780HZ27NjBoEGDaNu2LcuWLaNKlSr07t2b06dP/xfdEBEREXnuhIRH0HT+Ut4uU5yQob3Z9U57Np+7wBdbdwOw59JVJjd+jdDhfS0/3coUe2h7NyIi6bT4Nybu3G+1PyYunqbzlvJq3lwED+5F0LtvEXw3nEFrNwOw7tQ5tv1zmdPvd+OdssUZsm6zpW7/wE18XudVnB10p448n56bV+6ECRNwc3Oz2rdw4UKcnJwICAjAwcGB/Pnzc/78eaZNm0aLFi2SbGfatGnUrl2b9u3bAzBkyBD279/PrFmz+Pjjj9O8HyIiIiLPmyxurlwa1JN0zk4YhsGNiCii4+LJ4momOi6Ow9euUzpHtmS1FRYdQ9EJM2ldtCDNCr9idczJwZ6j776F2cEBOzsToVHRhMfGksXVDIDD/6+qeP+eMfv/314edApXJ0dey58ndTosYgPPxRWz3bt388svv/D5559b7d+zZw9ly5bF4V/fjFSoUIGzZ89y48aNRO0kJCSwb98+KlSoYLW/fPny7NmzJ22CFxEREXkBpHN2AiDfN9Mo9f1ssru70alkUf4ODiE2Pp5Rm7bh/eUU/L6bwZdbd5GQkPSSGy4ODhzo3YlvG9bC3ckx0XE3J0fs7Ey8Ov1nCn47nTvRMfSvXAaAWvnyUCt/bopMmMlP+4/wdb3qRMTEErDpL76qWz3N+i7yX3jmE7M7d+4wePBgPvzwQ7y8vKyOBQcHkz17dqt9WbNmBeDy5ctJthUREZFknStXrqRy5CIiIiIvnqPvduFc/x7Y25l4c+FKbkfH8KpPLvqUL8nZ/t2Z1bw+k3buZ9z2pL/0drC3I5u7W5LH/m1NxxZcHdKLolkzU2/2r8QnJGBnZ2Jy49pcHtyTPT07UCZndsZs2UnnkkW5HhFJzRm/UH7qXH7YfTC1uy2S5p75qYwBAQGUKFGCxo0bJzoWFRWFk5OT1T5nZ2cAoqOjkywPJFknqfIiIiIiYs3s6IjZ0ZExr1Wl8o8LmN2iAes6t7IcL+vtRd8KpVh0+AQDKpd96vOMq18D76+m8PfV65T0ympV5vj1m2w4c54/u7ah6vQFvFexNPVfyUuRCTOpkscbv6yZrMpHRkZiGFo8P7VERkZa/VfuMQwDk8mU4nrPdGK2bNky9uzZw8qVK5M87uLiQkxMjNW++wmWq6trovL3k7ak6pjN5tQIWUREROSFs/2fy3RfvpZ9PTvi5GAPQHR8PE729qw/fZ5bUdF0/9diH9Fx8ZgdU/4x81zoberMWsTmrm/ilc7dch6AjGaXROXf/20TX9erjoO9HUeuXaeUVzYyuDiTzzMDR0NuJErMzp49qyQiDZw7d87WITxzHrwQlBzPdGK2ePFibty4QfXq1a32jxw5kunTp5MjRw6uXbtmdez+drZsiW9A9fDwwNXVNck6D05vFBEREZF7/LNlJjI2juHr/2T0a1W5EhbOkHVb6FKyKI72dgxc8wf5M3pQI28udl68wsSd+/my7qspPk8ej/R4ml0YuGYzU5vUJiounr6rN1CvgA95PNJblV14+DjeGdyplDsnAAUyerLjwmUyubpw8kYo+TN6JGo/b968umKWiiIjIzl37hw+Pj66yPEvp06deqJ6z3Ri9tVXX1mmH95Xp04d3n33XRo0aMDq1av5+eefiY+Px97+3rc327dvJ2/evGTKlClReyaTiVKlSrFr1y5atfrfJfedO3dSunTptO2MiIiIyHPK3dmJle2bM3DNH3h/NYUMzs60KVaY4a+Wx9nBga/qRdJ39QYu3blLdnc3PqpekXbF/QDYev4ijecu5WDvTuR+ILl6kMlkYvGbTem/ZhMFxv+Ii4M9TQoV4JNaVazK3Y2O4bMtO1nX6X+f575rWJMey9cxZN1mepUvmWjaI6DkIY2YzeYkZ6u9rJ5kGiOAyXjOvjYoWLAgY8eOpXnz5ty4cYP69etTs2ZNunXrxt9//01AQACjRo2iWbNmANy9e5fY2FgyZswIwNatW+nRoweDBg2iWrVqLF68mHnz5rFkyRLy58//RDEdOnQIgELbfofgS48pLSIiIiL/qew5cXy7v62jeOFEREQQFBRE4cKFlZj9y/3cwN/fP0X1nvlVGR8lU6ZM/Pjjj5w9e5ZmzZoxceJEBg8ebEnKAEaPHk3Lli0t21WqVGHMmDEsWLCAZs2asWPHDqZMmfLESZmIiIiIiMjTeu6umD2LdMVMRERE5BmmK2ZpQlfMkvZSXjETERERERF5ESgxExERERERsbFnelXG543plUIYmROvACQiIiIitmPyzGjrEF5IJpMJs9n8xKsQijUlZqnIoWYDW4cgIiIiIvKfMJvN+Pn52ToM4g0D+xcgOVRilopGXz/P+dhoW4chIiIiIvJSyOPozPDMeWwdRqpQYpaKzsdGczIm0tZhiIiIiIjIc0aLf4iIiIiIvOSM+Hj2v9GVoAEjEh27vfcgm33LPbJ+fFQUxz/4lL/K1GJrsaocaNuDsKATANzatY8tfhWtfjb7luUPnxJEX72GER9PUP8P+bNoZXbWfJ1bu/db2o385yK767UmITomdTv8DFJiJiIiIiLykjv37VRu/yshAjAMgysLl/F3x54YMY9OjM6Nm0Lk2fOUW7+ESns24l7Yl8P//+w4j3KlqHZ0u+Wn0q71mPPkwmdAb5yzZeXmlm3c3r2fCn8FkrNDa06P/sbS7smAz8k//H3snJ2S1Y/Q0FB69epFrly5cHNzw8vLi06dOnHx4kVLmRs3btC/f38KFCiAu7s7OXLkoF27dhw+fNiqrejoaIYNG0b+/Plxd3cnS5YstGjRgqCgoETnvXHjBq6urpQoUSJZcSZFiZmIiIiIyEssdNsuQgLXk6V+Lav9xweN5MqCJfi83/OxbUScPouRkACGAYaByd4Oe7NLkmVPjvwM5+xZ8enbHQCTvb3VcZP9vRQlZO1G7M1mMlatmOy+vPHGG1y/fp3du3cTHh7OgQMHiI6Opnbt2sTFxXH58mWKFy/OP//8w8qVK7lz5w4HDhwgX758lC9fnt9//93SVt++fdm2bRsbNmwgLCyMkydPkitXLqpVq8atW7eszvvjjz9Sv359rly5wvbt25Md77/pHjMRERERkZdUzPWbHB8cQNEfxnFh+lyrYz4DeuPilY3Q7bsf206ubh043HMgf5WsDvb2OHp6UOLnaYnK3dq1j5BV6yi3Yalln2eVCnhWqcCuGk1xypaFgp99RHxkJOe+mUyx2ZNT1J+tW7cyffp0smfPDkC2bNkYP348Q4cOJTQ0lP79+5MnTx4WLlyInd29BDBr1qx88sknxMXF0blzZ86fP4+DgwNbt26lQ4cO+Pj4AODh4cGXX37JnTt3CA4OxsPDA4CEhAS+//57vvnmGwoVKsSsWbOoWDH5yeR9umImIiIiIvISMhISCHr/A7y7dcDdr2Ci4y5e2ZLfVnw8WerVouKOtVQ5uIXMdWpwuHs/4qOsVyw/N34KOdq3wsU7h2Wfyc6OgmNHUHnfJsoGLiR98aKcnzCN7K2aEnvzFvtbv8WeRm24NHfRY+No06YN77zzDr169WLhwoWcP3+e7Nmz89NPP+Hp6cnSpUvp2LGjJSn7t+7du3P58mW2bdtmaWvUqFF07tyZOXPmcOLECRwdHZkxYwaFChWy1FuxYgXx8fE0adKEnj17smvXLk6cOJHssbtPiZmIiIiIyEvon8nTsXN2xrtzm6dqJyE2liO9BpG9VVOcs2fDwd2NV0YNITr4GqFbd1jKRZ6/wK0de8j5mPNFnD5H6NYd5OzchuNDAsjRrhUl5v/AufHfE37y9CPrTps2jUmTJvHPP//Qo0cPfHx8KFCgAPPmzeP69evExMSQO3fuJOt6e3sDcOnSJQBGjBjBokWLCA8PZ8CAARQsWJCcOXMybtw4q3oTJ06kT58+ODg44O3tzWuvvcacOXMeO24P0lRGEREREZGXUPCS1cRcC+FP/yoAJERFAXB93SaqHtqa7HbiIyKJu33HaoEQk709Jjs77BwdLftCAteToUwJzLlyPrK9kwGfUeCjQdg5OBB+/DTp/P1wSJ8Oc25vwk+cxu2V/A+ta2dnR/v27Wnfvj2GYRAUFMScOXPo0KEDgYGBODo6cv78+STrXr58Gbg3/fG+xo0b07hxYwBOnz7NkiVLGDp0KOnTp6dr164EBQWxYcMG9uzZw5dffglAREQEcXFxXLlyBS8vr8eM3r9iT3ZJERERERF5YZTfuIyqh/+i6qGtVD20laxN6pO1Sf0UJWUAjhnSk6FsSU5/9i0x128SHxXNmc/G4+jpQYayJS3lbu85QIZypR7Z1rWVa3H2yk6GMvfqmfPm5vbeA8SG3iLi7D+Y8yR9tQtg7dq1uLu7c/PmTQBMJhN+fn6MHTuWkiVLcujQIZo1a8aMGTNISEgA4ObNm0yaNInw8HCmT59OtmzZqFy5MkFBQbi4uFit1Jg/f34GDRpEo0aN2L//3gqWEydOpEGDBhw+fJgDBw5w4MABli9fjre3NxMmTEjROCoxExERERGRFLn/bLKoS1cAKDL5K1zz5WF3vVZsr1CH8JNnKDb7e+xdzZY6kf9cxDlb1oe2GRcWzvlJP5Jv6HuWfa98PIx/Jv3Izpqv493pTdIVLfTQ+tWqVSNbtmx06dKFQ4cOERsby927d5k3bx4nT56kYcOGjB8/nuDgYJo1a8bRo0cJCwtjyZIl+Pr68vnnnzNjxgycnZ0pVKgQpUuX5u2332bXrl1ERUURERFBYGAgmzZtolmzZty5c4fZs2fTvXt3vL29LT/Zs2enRYsWTJkyhfDw8GSPqckwDCPZpSVJhw4dAmBCZmdOxkTaOBoRERERkZfDK05mfvDytWxfuXKFgIAA1q5dy7Vr13BycqJixYoEBARQvnx54N4zxz799FOWL1/O1atXSZcuHa+++iqXL1/GycmJL7/8klKlSnH79m1LuUuXLmFvb0+JEiUYNmwY9evX57vvvuPTTz/l0qVLOP5ryuahQ4e4desWtWvX5osvvuDdd99NVl+UmKUCJWYiIiIiIv+9BxOzp2EYBqtXryZv3rwUKVLkidu5nxv4+/unqJ4W/xARERERkZeeyWSiUaNGNju/7jETERERERGxMSVmIiIiIiIiNqapjKkoj6OzrUMQEREREXlpvEifv5WYpaLhmfPYOgQRERERkZdKvGFgbzLZOoynpqmMqSQmJobISK3ImBYiIyM5evSoxjeNaHzTlsY37WmM05bGN21pfNOWxjdtPSvj+yIkZaDELFXpyQNpwzAMIiMjNb5pROObtjS+aU9jnLY0vmlL45u2NL5pS+ObupSYiYiIiIiI2JgSMxERERERERtTYiYiIiIiImJjSsxERERERERsTImZiIiIiIiIjSkxExERERERsTElZiIiIiIiIjamxExERERERMTGlJiJiIiIiIjYmBIzERERERERG1NiJiIiIiIiYmNKzERERERERGxMiZmIiIiIiIiNKTETERERERGxMSVmqchkMtk6hBeSyWTCbDZrfNOIxjdtaXzTnsZYREReBA62DuBF4eTkhNlstnUYLySz2Yyfn5+tw3hhaXzTlsY37aXmGCcYCdiZ9J2liIj895SYpaI14Wu4GX/T1mGIiMgTyGifkXpu9WwdhoiIvKSUmKWim/E3CYkPsXUYIiIiIiLynNF8DRERERERERtTYiYiIqni0uFLTG42mQ/yfcCIQiOY23MuYTfCrMqc3XWWgV4DH9vWhu82MLLISAZ7D2ZC4wlcPXk1yXJz35nLhMYTLNsJ8QnM7TmXIbmHMKb8GM7sOGM5dv3cdb6o+gVx0XHJ6o+Pjw8uLi64u7uTLl063NzcyJEjB4MGDSIhIQGAmTNnUqpUKdKlS0eGDBmoWrUqq1evtmrHMAx++OEHypYti7u7O56enlSuXJnZs2cnKw4REXk5KDETEZGnFhMZw9TWU8lbLi8fH/uYoduGEnEzggV9FgD3kpMdc3cwpeWUxyZGuxbsYsvULbzz6zuMPjWaXMVzMbPTTAzDsCq3Y+4O9v6612rfsY3HOLvjLCMPjaTyW5VZPmK55diSoUto+nFTHJyTP4t/ypQphIWFcffuXcLDw1m7di2zZs1i1KhRzJ8/n2HDhjF58mRu375NSEgI3bt3p3nz5mzZssXSRrt27Rg7dizDhw/n6tWrXLt2jSFDhjB8+HC6du2a7FhEROTFpnvMRETkqYVeDCVHkRzUHVwXO3s7HDI6UKlzJea+MxeABX0WcPXkVeoNqWeVLCVl++ztVOlaBa/CXgA0HtmY7bO3c2rrKV6p+goAwceCWffVOip2rGh1Nc3O/v+/bzSst/9e/TdOrk4UrFHwqfrp7+9PtWrV2LdvHyEhIRQvXpwKFSoA91bn7dixI+fPnyc0NBSA5cuXs3jxYoKCgsiXL5+lnSZNmlCgQAGKFStGy5YtqV+//lPFJSIizz9dMRMRkaeW7ZVsvLPonf8lRsCBFQfIVSIXAA0+aMD7697Hu7j3Y9sKPhaMl5+XZdve0Z4s+bNw6fAl4N7VuVldZ9Hyy5aky5rOqq5vdV98q/syuuxods7bSbMxzYiJiCFwTCDNRjd7qj7Gxsbyxx9/sHHjRurUqUPLli3ZuHEj9erVY+LEiezZs4fY2FhGjBhB06ZNAVi6dCmVK1e2Ssru8/Pzo1KlSixatOip4hIRkReDrpiJiEiqMgyD38b8xpE1R+i7ui8AHjk9kl0/OiwaJ1cnq31OZidiwmMAWDx4MQVrFMSvth/n9563KmdnZ8cb497gjXFvWPat+mQV5duVJ/xmOLO6zSImIoaKHStSuUvlx8bSq1cv+vXrZ9n29vZmwIAB9OnTB5PJxN69e5k8eTLjx4/n9OnTuLu70759e7788kvc3d25fPky2bNnf2j7OXLk4PLly8kZFhERecEpMRMRkVQTdSeK+X3mc+HgBfqu7ksOvxwpbsPJ1YnYyFirfTGRMTi7O7Nn0R4uH7nMe2veS1ZbV09e5fgfx+m3th/j646neq/q+NX2Y3TZ0eSvmJ/shR6eNAFMnjyZzp07P/R4sWLFmDJlCgAhISGsX7+ewYMHExYWxpw5c/Dy8uLUqVMPrX/27Fk9gFxERABNZRQRkVRy/ex1vn7ta6LuRjFg44AnSsoAvAp7ceXYFct2fGw8IadD8Crsxe6fd3Pt1DU+9P2QoT5D2fDtBs7uOMtQn6GEXgxN1NaSIUtoNroZ9g72BAcFk6t4LszpzWT2yUzwseAn7itA7ty5mTRpkmU7S5YstGnThsGDB7N//34AWrVqxe7duzl06FCi+vv372ffvn20aNHiqeIQEZEXg66YiYjIU4u4FcGkppN4peorvDnhTezsnvx7v/LtyhP4WSCFaxUma4GsrP50NemypiN/pfz4vuprVTbws0BO/XWKviv7Jmpn35J9eOT0IF+Fe/d3Zc6XmbO7zuKW0Y2Q0yFkzpf5iWMEaN++PZ9++il58uShatWquLm5ceTIEaZPn25Jtho1akS7du1o3Lgx3333HbVq1QJg/fr19O3blzZt2tCoUaOnikNERF4MSsxEROSp7Zy3k9CLoRxYfoADKw5YHfviwhePrHt6+2mmtp7KsO3D8PT2pHz78kTejmRGhxmE3Qgjd8nc9Pi5B/aO9smOJ+puFL9/8zu9l/e27Gv5ZUsW9F3Aio9WULV7VbyLPX4hkkcZPXo0OXLkICAggOPHj5OQkEDevHnp1q0b7733v6mWM2fOZMaMGYwdO5aOHTsC9xb+GDVq1COnSYqIyMvFZDz4YBhJsftTVA7lOURIfIiNoxERkSeRxT4LbdO3tXUYz5yIiAiCgoIoXLgwrq6utg7nhaPxTVsa37Sl8U3a/dzA398/RfV0j5mIiIiIiIiNKTETERERERGxMSVmIiIiIiIiNqbFP1KRj6MPnnaetg5DRESeQAb7DLYO4ZlkMpkwm82YTCZbhyIi8kJTYpaKKpkr2ToEERGRVGU2m1/qh2AbCQmYnuLxDyIiyaXELBXFLZmHEXLV1mGIiIhIKjBlyYZD83a2DkNEXhJKzFKREXIVgi/ZOgwRERFJBXqekIj8l3RtXkRERERExMaeiytmly5dombNmon2f/rpp7Rq1YqgoCBGjx7N4cOH8fDwoEOHDnTt2vWRbQYGBjJhwgQuXLiAj48PgwYNolq1amnVBRERkZfOweAQhq7bzL7LV3Gyt+e1/Hn4sm51MruZ+eXQMT7dvIPLd8LI5u5Kv4ql6VG2eJLtJCQYZBo7EQMDE/9bhOTioHdwc3LkzM1b9PttIzsvXsHBzo46BXwYV78GHmYX4hMS6LZsLSuOncYrnRtTm9Shcp6cAJy5eYtWv6xgW/e2ODs8Fx+JROQF9ly8Cx0/fhxnZ2fWr19vtSpUunTpCA0NpUuXLrz22muMGjWKAwcOMGrUKDw8PGjRokWS7e3YsYNBgwYxdOhQKlasyK+//krv3r1ZtmwZ+fPn/6+6JSIi8sKKjI2lydwlvFXan+Vtm3E3Joa3lq6h2/K1fFqrCm+vWMfaji0pnysH2/+5TO1Zi/DLmokqebwTtXU05AaxCQncHNYHJwf7RMc7LP6NqnlysrhNU+5Gx9DqlxUMXreZH5rWZd2pc2z75zKn3+/G3INHGbJuM1u7twWgf+AmPq/zqpIyEXkmPBdTGU+cOEHevHnJmjUrWbJksfy4uLiwcOFCnJycCAgIIH/+/LRo0YLOnTszbdq0h7Y3bdo0ateuTfv27cmfPz9DhgyhSJEizJo16z/slYiIyIvrn9t3KZY9Cx++WgEnB3syuZrpVroYW89f5OSNUOISDBIMMAwDkwnsTSZcHpIg7b0cjH+2zEkmZQDHQm6SYECCYWAAdiYTro6OADj8/4qK9+8Xs///7eVBp3B1cuS1/HlStd8iIk/qufiK6Pjx4xQoUCDJY3v27KFs2bI4/OvNvEKFCkydOpUbN26QKVMmq/IJCQns27ePoUOHWu0vX748v//+e+oHLyIi8hIqmDkjK9s3t9q35OgJSnllo04BH8p7Z+fVGT9jbzIRbxh8XqcaZXJmT7KtPZeuEhkbR8Uf5nH+1h0KZc7I6NeqUjF3DgBGVK/I8PV/MmHHPuINg/LeXox5rSoAtfLloVb+3BSZMJMc6dyZ0qQ2ETGxBGz6i9Xtk55ZIyJiC89FYnbixAmyZMlC27ZtOXfuHHny5KFXr15UrVqV4OBgfH19rcpnzZoVgMuXLydKzO7cuUNERATZs2dPVOfKlStp2xEREZGXkGEYjNy4jdUnzrChS2ui4+Lw8cjAB9UqUM3Hm99Pn6fdotUUzZqZ2gV8EtU3OzpQzjs7I2tUIqPZhe93HaTh3MXs7dmRvJ4ZsDOZ+ODVCvSrWJrrEZG0/3U1vVat56fm9bGzMzG5cW0mN65tae/D9VvpXLKopWx4bCxdS/k/9B43EZH/wjOfmMXExHDu3DnMZjODBw/G1dWVFStW0L17d2bOnElUVBROTk5WdZydnQGIjo5O1F5UVBRAknWSKi8iIiJP7k5UNN2Wr2X/5Wts6NIa/2xZ6PfbRlwcHKj1/9MIG/jm4w3/gkzb+3eSidkXdV+12u5fuQyzDhwh8MQZKuTKwciNfxEytDcO9na4OTnyeZ1q1JjxC981qEl6F2erusev32TDmfP82bUNVacv4L2Kpan/Sl6KTJhJlTze+GW1/kIXIDIyEsNIm8XzIyMjrf4rqUvjm7Y0vkm7N0Xb9PiCD3jmEzMnJyd2796Ng4ODJZkqWrQop0+fZvr06bi4uBATE2NV536C5erqmqi9+0lbUnXMZnNadEFEROSldPrmLZrMW0ruDOnY3qMdmd3u/Tv7z+27ZDS7WJV1tLPHyT7pe8hGbNhKcz9fSnplteyLiYvD7OjAP7fvEG8kEG8k4PD/t8472tlhMpks95f92/u/beLretVxsLfjyLXrlPLKRgYXZ/J5ZuBoyI0kE7OzZ8+m+QfPc+fOpWn7LzuNb9rS+Cb24EWg5HjmEzNIOsHy9fVl69atZM+enWvXrlkdu7+dLVu2RPU8PDxwdXVNss6D0xtFRETkyYRGRlF31iKq583ND03qYGf3v2+PGxfMR7/fNtG6aEFq58/Dn+cvMv/vIGa3aJBkW0eu3eCv85uY36oRnmZnvty6mzvRMTQtVIB4w8DV0ZGBazbzZd1XuRMdw4cbtvJ64QK4OjlatbPw8HG8M7hTKfe95fILZPRkx4XLZHJ14eSNUPJn9Ejy/Hnz5k3TK2bnzp3Dx8dHXxCnAY1v2tL4Ju3UqVNPVO+ZT8yOHTtGmzZtmDZtGmXKlLHsP3z4MAUKFKBw4cL8/PPPxMfHY///37Rt376dvHnzJrq/DMBkMlGqVCl27dpFq1atLPt37txJ6dKl075DIiIiL4FZ+4/wz+27/HrkOIuPnLA6Fjq8LxGxcbwfuIngu+HkypCOCY1q0bBgPgC2nr9I47lLOdi7E7k90vNj0zoMXreFMlPmEB4TS9mc2Qns2JKMrvc+CP7WoQUf/P4neb6eitnRgUYF81sW/7jvbnQMn23ZybpO//u3/7uGNemxfB1D1m2mV/mSVlfk/u2/+MBpNpuT/CJaUofGN21pfK09yTRGAJORVl8BpZKEhATefPNNIiMjGTlyJJ6enixcuJD58+fz66+/kjlzZurXr0/NmjXp1q0bf//9NwEBAYwaNYpmzZoBcPfuXWJjY8mYMSMAW7dupUePHpaHSi9evJh58+axZMmSJ3qO2aFDhwAotO13CL6Uep0XERER28meE8e3+6fpKSIiIggKCqJw4cL6YJsGNL5pS+ObtPu5gb+/f4rqPfPPMbOzs2PKlCn4+/vTr18/mjVrxsGDB5k5cyYFCxYkU6ZM/Pjjj5w9e5ZmzZoxceJEBg8ebEnKAEaPHk3Lli0t21WqVGHMmDEsWLCAZs2asWPHDqZMmaKHS4uIiIiIiE0881MZATJmzMiYMWMeerxYsWL88ssvDz3+2WefJdr3+uuv8/rrr6dGeCIiIiIiIk/lmb9iJiIiIiIi8qJTYiYiIiIiImJjz8VUxueF6ZVCGJmTXtFJREREni8mz4xpfw6TCbPZ/MSruMmjaXzTlsY3dT3zqzI+D5505RURERERkRdBvGFgrwQNePLcQFfMUtHo6+c5Hxtt6zBERERERP4zeRydGZ45j63DeO4pMUtF52OjORkTaeswRERERETkOaPFP0REREREngFGfDz73+hK0IARln139h9ib9P2bPGryI4qDbjyy9JktXUy4HOrdgDuHjnGgTbd+dO/CltLVifo/eHEht6ynDuo/4f8WbQyO2u+zq3d+y31Iv+5yO56rUmIjknWuX18fHBxccHd3d3qx2Qy4ezsTEREhKVsVFQU7u7u5MyZk3/fYXXmzBlMJhOHDx+27Js0aRImk4lx48YlK47njRIzEREREZFnwLlvp3L7XwlR7O07/N2lD9lbNKLK339S8IsATn3yFXcOHHpoG7Ghtzja7wMu/bTAan9CTCyHuvTFo2JZKu//g/J/rCD62nVOffIVADe3bOP27v1U+CuQnB1ac3r0N5a6JwM+J//w97Fzdkp2X6ZMmUJYWFiiH5PJxNatWy3l1q9fj4+PD2FhYezcudOy//fff8fHx4eiRYta9k2aNImePXvy7bffEhcXl+xYnhdKzEREREREbCx02y5CAteTpX4ty76QwPU4emYgZ8c3sXNwwLNSObI1bcCl2b8k2UZceAQ7a76OQ/p0ZK7/mtUxOydHyv+xgjx9umHn4EDcnbskRETimOne6qMme3ur8ib7e2lCyNqN2JvNZKxa8an76ObmRvXq1dm4caNl37Jly2jUqBH16tVj2bJllv3r16+ncePGlu0NGzZw7do1vv76axISEvj111+fOp5njRIzEREREREbirl+k+ODA/D7dix2Li6W/REnTuNW8BWrsq6v5CMs6ESS7dg5O1Fu3WJ8Px6Gvas50XF7VzMmOzv2tejEzqoNiQsLI3ePTgB4VqmAZ5UK7KrRlCsLl1Hgo0HER0Zy7pvJFPhoYKr1tWHDhpbELCEhgZUrV9K0aVOaNGliScwSEhLYuHEjTZo0sdSbMGEC3bt3x2w206tXL77++utUi+lZocRMRERERMRGjIQEgt7/AO9uHXD3K2h1LC48IlGCZW92IT4i6cXm7BwccMqS6bHnLD5vKpUPbsGt4CscbP82Rnw8Jjs7Co4dQeV9mygbuJD0xYtyfsI0srdqSuzNW+xv/RZ7GrXh0txFyepXr1698PDwsPoJDw+nYcOG7Nu3j9u3b7Nt2zZMJhPly5enQYMGnD59muPHj7Nv3z7i4uJ49dVXATh//jxr1qyhd+/eAPTo0YMjR46wefPmZMXyvNCqjCIiIiIiNvLP5OnYOTvj3blNomP2Zhei79y12hcfGYWDm+tTndPexQV7FxdeCRjCtjI1CQs6SbqihazKRJw+R+jWHZRcMpv9zTvi3bUDmWpUYWfNpniUL4XbK/kfeY7JkyfTuXPnRPvz5cvHK6+8wubNm9myZQuNGzfGzs4OT09PqlatSmBgIJGRkdStWxdHR0dLW7GxsZQoUcLSTmxsLF999ZUleXsRKDETEREREbGR4CWribkWwp/+VQBIiIoC4Pq6TeT/4H1C/9xhVT7i5BncChZI8XkiL1ziYJvulFwyC+esWe6dK+beKouOHukTlT8Z8BkFPhqEnYMD4cdPk87fD4f06TDn9ib8xOnHJmaP0rBhQ/78809Wr17Nl19+adnfpEkTNmzYQEREBF26dAHurdo4ffp0pk+fzmuv/e++ucOHD9OgQQOOHTtGoUKFEp3jeaSpjCIiIiIiNlJ+4zKqHv6Lqoe2UvXQVrI2qU/WJvWpemgrWerVIibkOhemzyUhNpbQbbu5uvw3srd6PcXncfHOgYNHBk59/BVx4RHE3Azl5IgxZKxeGRfvHFZlr61ci7NXdjKUKQmAOW9ubu89QGzoLSLO/oM5T+6n6nPDhg1ZsmQJFy5csEq2mjZtyq5du9i1axf169cHYP78+ZhMJtq1a4e3t7flp169evj7+/PNN9887DTPHSVmIiIiIiLPIEdPD4rNnULIb7/zV8nqHB86igIjB+NZqSwAt3btY4tfRaIuXXlsWyaTiaLTxmPExbGjcn321G+Nc47s+H33mVW5uLBwzk/6kXxD37Pse+XjYfwz6Ud21nwd705vJpr2mFJVqlThxo0b1K5dG5d/LXaSN29esmbNSsmSJcmU6d69cpMmTaJdu3aWaY3/1qNHD+bMmcO1a9eeKp5nhcn495Pc5IkcOnTvWRITMjtzMibpmzFFRERERF5ErziZ+cHL19ZhPDPu5wb+/v4pqqcrZiIiIiIiIjamxExERERERMTGlJiJiIiIiIjYmBIzERERERERG9NzzFJRHkdnW4cgIiIiIvKf0mfg1KHELBUNz5zH1iGIiIiIiPzn4g0De5PJ1mE81zSVMZXExMQQGaml8tNCZGQkR48e1fimEY1v2tL4pj2NcdrS+KYtjW/a0vimrX+Pr5Kyp6fELBXpkXBpwzAMIiMjNb5pROObtjS+aU9jnLY0vmlL45u2NL5pS+ObupSYiYiIiIiI2JgSMxERERERERtTYiYiIiIiImJjSsxERERERERsTImZiIiIiIiIjSkxExERERERsTElZiIiIiIiIjamxExERERERMTGlJiJiIiIiIjYmBIzERERERERG1NiJiIiIiIiYmNKzERERERERGxMiZmIiIiIiIiNKTETERERERGxMSVmqchkMtk6hBeSyWTCbDZrfNOIxjdtaXzTnsZYREReBA62DuBF4eTkhNlstnUYLySz2Yyfn5+tw3hhaXzTlsY37aXGGCcYCdiZ9F2liIjYjhKzVLQmfA0342/aOgwREUmBjPYZqedWz9ZhiIjIS06JWSq6GX+TkPgQW4chIiIiIiLPmVSZtxESEsKRI0eIj49PjeZEREREREReKilOzMLDwxk2bBhz5swB4LfffqNGjRq0bNmSRo0aceXKlVQPUkREnl2XDl9icrPJfJDvA0YUGsHcnnMJuxEGwLk95/jmtW8YnGswH5f4mB1zdjy0nbjoOFYErGBkkZEMyzuM6R2mE3ox1HL84qGLTGo6iaF5hjK8wHDmvjOX8JvhACTEJzC351yG5B7CmPJjOLPjjKXe9XPX+aLqF8RFxyWrPz4+Pri4uODu7k66dOlwc3MjR44cDBo0iISEBABmzpxJqVKlSJcuHRkyZKBq1aqsXr3aqh3DMPjhhx8oW7Ys7u7ueHp6UrlyZWbPnp28gRURkZdKihOzr776irVr1+Lp6QnA119/TaFChZg4cSIODg589dVXqR6kiIg8m2IiY5jaeip5y+Xl42MfM3TbUCJuRrCgzwIibkXwwxs/UPbNsow9O5Y3v3uTpcOXcn7v+STbWvnxSv5e+Tfv/PoOnxz/hCz5svB9i++Ji4kjLiaOH974gQJVCjD69GiG7x3Onat3WPbhMgCObTzG2R1nGXloJJXfqszyEcst7S4ZuoSmHzfFwTn5s/enTJlCWFgYd+/eJTw8nLVr1zJr1ixGjRrF/PnzGTZsGJMnT+b27duEhITQvXt3mjdvzpYtWyxttGvXjrFjxzJ8+HCuXr3KtWvXGDJkCMOHD6dr165PNuAiIvLCSvE9Zhs2bGDo0KE0atSIoKAgLl26xODBg6lVqxZxcXGMHDkyLeIUEZFnUOjFUHIUyUHdwXWxs7fDIaMDlTpXYu47czm44iBuGd2o2q0qAL7VfCndqjRbp28lT+k8idrat3gfjQMa41XYC4BGHzVi64ytnNh8Ar/afgzfPRxHsyN2dnZE3ookOjwa90zuANjZ///3jAZW23+v/hsnVycK1ij4VP309/enWrVq7Nu3j5CQEIoXL06FChWAe6vyduzYkfPnzxMaeu8K3/Lly1m8eDFBQUHky5fP0k6TJk0oUKAAxYoVo2XLltSvX/+p4hIRkRdHiq+Y3bp1y/KPzB9//IGDgwOVK1cGIEOGDERHR6duhCIi8szK9ko23ln0zv8SI+DAigPkKpGL4GPBliTrvuwFs3Pp8KUk20qIT8DZ1fl/O0z3nlF27eQ1AJzdnLGzs+Pbet/ySclPiL4bTc2+NQHwre6Lb3VfRpcdzc55O2k2phkxETEEjgmk2ehmT9XH2NhY/vjjDzZu3EidOnVo2bIlGzdupF69ekycOJE9e/YQGxvLiBEjaNq0KQBLly6lcuXKVknZfX5+flSqVIlFixY9VVwiIvJiSXFiljNnTo4fPw7AunXrKFGiBO7u976x3Lx5M97e3qkboYiIPBcMw2D16NUcWXOEZmObER0WjZObk1UZJ7MTMeExSdYv3rg4v3/zO9fPXic2KpbAMYHERsYSGxVrVa7n0p6MOTMGLz8vJjebTEJ8AnZ2drwx7g1GnxzN4C2DyV0qN+u+Xkf5duUJvxnOdw2/46saX/HXzL+S1ZdevXrh4eGBh4cHWbJkoU+fPgwYMIA+ffpQs2ZN9u7di4+PD+PHj6ds2bJkzJiRnj17EhZ27966y5cvkz179oe2nyNHDi5fvpysWERE5OWQ4sSsbdu2fPbZZ9SvX5+goCDatm0LQN++ffnpp5948803Uz1IERF5tkXdiWJmp5nsWbiHvqv7ksMvB06uTsRGWCdVMZExOLs7J9lG00+a4lPOhwmNJjCm3BgcnB3w8vPC7GG2KudkdsLVw5XmnzXnStAVLh9JnOBcPXmV438cp2qPqix4dwGV36pM7+W9CfwskOBjwY/tz+TJk7l165bl5/DhwwwfPhyTyQRAsWLFmDJlCqdOneLatWv88MMPrFq1ip49ewLg5eXF+fNJ30sHcPbsWby8vB56XEREXj4pTsw6dOjAZ599Rrly5fjmm29o0KABAA4ODgQEBNCuXbtUD1JERJ5d189e5+vXvibqbhQDNg4gh18OALwKe3HlmPVKvcHHE09vvO/2ldvUGVCHUUdGMfLvkVTtXpVrJ6+Ru0Rubvxzg49LfMzt4NuW8vdXWXT1dE3U1pIhS2g2uhn2DvYEBwWTq3guzOnNZPbJnKzE7FFy587NpEmTLNtZsmShTZs2DB48mP379wPQqlUrdu/ezaFDhxLV379/P/v27aNFixZPFYeIiLxYUpyYxcbG0rBhQ0aNGmVJygDGjRvHG2+8wc6dO1M1QBEReXZF3IpgUtNJ5C2bl3cWv2NZjAOgWONi3L12lz++/4P42HhO/nmSvYv2Ur5d+STb+uP7P5jfez7RYdFE3Ipg0cBFeBf3Jnep3GTMlRFXT1eWDV9GdFg0YTfC+HXQrxR+rTAZc2W0amffkn145PQgX4V793dlzpeZs7vOEn4znJDTIWTOl/mp+ty+fXs+/fRTVq1axe3bt4mLi+PgwYNMnz7dkmw1atSIdu3a0bhxY1asWEF4eDjh4eEsX76cpk2b0qZNGxo1avRUcYiIyIslxasy9uzZk8mTJ+PkZH3fQFhYGJ9//jm//vorQUFBqRagiIg8u3bO20noxVAOLD/AgRUHrI59ceELei7pydJhSwkcG4h7Zneaf9acV6q+AsDp7aeZ2noqw7YPw9PbkyYjm7BwwEJGFR8FQOFahek2rxtwbxGQbnO7sWTYEkYVH4WjiyP+DfxpOKKh1Tmj7kbx+ze/03t5b8u+ll+2ZEHfBaz4aAVVu1fFu9jT3Qs9evRocuTIQUBAAMePHychIYG8efPSrVs33nvvPUu5mTNnMmPGDMaOHUvHjh2Bewt/jBo1is6dOz9VDCIi8uIxGYZhpKRC2bJlKV68uFVytn79ej7++GNu3rxJx44dGTx4cJoE+6y6P1XlUJ5DhMSH2DgaERFJiSz2WWibvq2tw3hmRUREEBQUROHChXF1TTxtVJ6OxjdtaXzTlsY3afdzA39//xTVS/FUxlmzZnH06FF69uzJhQsX6Nu3L3369CFnzpwsWbLkpUvKREREREREnlaKpzL6+fkxZ84cOnfuTN26dUmfPj2ffPIJrVq1Sov4REREREREXngpvmIGkD9/fubPn0+OHDnIly+fbmAWERERERF5Csm6YlazZk3Ls1v+LSwsjEuXLlGzZk3LvFKTycT69etTN0oREREREZEXWLISs3LlyiWZmIk1H0cfPO08bR2GiIikQAb7DLYO4ZlmMpkwm836HJBGNL4icl+yErPPPvssreN4IVQyV7J1CCIiIqnKbDbj5+dn6zBeWGk5vkZCAia7J7prRURsIMWLf8C9KYzh4eFky5aNmJgYZs+eTXBwMHXr1qVs2bKpHeNzI27JPIyQq7YOQ0RERF5ypizZcGjeztZhiEgKpDgx+/vvv+nWrRutW7dm4MCBfPrppyxcuJD06dMzf/58JkyYQK1atdIi1meeEXIVgi/ZOgwRERF5yaXoIbUi8kxI8fXtcePGkS9fPt544w2ioqJYuXIlbdu2ZdeuXbRs2ZIpU6akRZwsW7aMBg0a4O/vT8OGDQkMDLQcCwoKon379pQoUYLq1aszffr0x7YXGBhoaa9x48Zs2bIlTeIWERGR59OmM/9Qedp8Mo2ZSK4vp9Dvt41ExsZaldlx4TLpPvn2ke1ExcYxIHATeb/+gcxjJ1J52nz+OPuP1fH+gZvI9eUUMo+dSN1ZizgWchOA+IQEuiwJJNOYiRSdMJO/zv/vC+AzN29R+vvZRMfFpWKvRcRWUpyYHTx4kJ49e5IrVy62b99OVFQUTZs2BaBBgwacPHky1YNcvnw5H3zwAW+88QarVq2iQYMG9O/fn/379xMaGkqXLl3w8fFh8eLF9O3bl2+//ZbFixc/tL0dO3YwaNAg2rZty7Jly6hSpQq9e/fm9OnTqR67iIiIPH9CwiNoOn8pb5cpTsjQ3ux6pz2bz13gi627ATAMg5/2HabBnMVEx8c/sq3h6/9k24XLbOnWhqtDevFWqaK8Pn8Z/9y6A0Cf1RvYd/kqu95pz6VB71Aoc0beXLgSgHWnzrHtn8ucfr8b75QtzpB1my3t9g/cxOd1XsXZ4YnuTBGRZ0yK/5Lt7OxwcnICYPPmzaRPn55ixYoB9+49c3FxSdUADcPg22+/pVOnTnTq1AmA3r17s2/fPnbt2sWuXbtwcnIiICAABwcH8ufPz/nz55k2bRotWrRIss1p06ZRu3Zt2rdvD8CQIUPYv38/s2bN4uOPP07V+EVEROT5k8XNlUuDepLO2QnDMLgREUV0XDxZXM0AdF++juPXb/JR9UoM/leylJTIuDhG1qhErgzpAOhauhgf/P4n+65cxcXBgXkHj/J3n854pXMHYEztapy4fhPDMHD4/8U77k9NtP//7eVBp3B1cuS1/HnSoPciYgspTsyKFi3Kr7/+iouLC4GBgVSvXh2TycSNGzeYNm0aRYsWTdUAz5w5w6VLl2jcuLHV/vvTFbt3707ZsmVx+Ne3RRUqVGDq1KncuHGDTJkyWdVLSEhg3759DB061Gp/+fLl+f3331M1dhEREXl+pXO+90V0vm+mceluGFVy56RTyXufcwJqVMI7Qzo2n73w2HYmN65ttb3pzD/cjo6hePas7LtyFQ8XZ3ZevELLn1dwPTyCSrlz8nW9e5+vauXLQ638uSkyYSY50rkzpUltImJiCdj0F6vbJ/0FtIg8n1I8lXHw4MFs376dNm3aYG9vT8+ePQFo1KgR586do1+/fqka4Llz5wCIiIiga9euVKxYkVatWrFx40YAgoODyZ49u1WdrFmzAnD58uVE7d25c4eIiIgk61y5ciVVYxcREZHn39F3u3Cufw/s7UyWKYbe/3/1K6V2XrhMm0WrGFG9Ink9M3AzMopbUdEsPXqS9Z1bcfTdt3BzcqTZgmXEJyRgZ2dicuPaXB7ckz09O1AmZ3bGbNlJ55JFuR4RSc0Zv1B+6lx+2H0wNbssIjaQ4itmfn5+rFu3jtOnT/PKK6/g6uoKQEBAAKVKlSJLliypGmBYWBhwb7phnz59GDhwIGvXrqVXr17MnDmTqKgoy9TK+5ydnQGIjo5O1F5UVBRAknWSKi8iIiIvN7OjI2ZHR8a8VpXKPy4gNDIKT3PKb92YsfcQA9b8wcgalehXqTQAzvb2xBsGn9epRha3e5+pvqz7Kjm/nMLx66H4ZbWe+XP8+k02nDnPn13bUHX6At6rWJr6r+SlyISZVMnjnah8ZGQkhvHyrtEYGRlp9V9JXRrfpBmG8UQPjX+iu0Xd3d0pXry41b66desCcPr0afLnz/8kzSbJ0dERgK5du9KsWTMAChcuzNGjR5k5cyYuLi7ExMRY1bmfYN1PGv/tftKWVB2z2ZxqcYuIiMjza/s/l+m+fC37enbEycEegOj4eJzs7XH7/88myRWfkEDf1RtYFnSKX99sQq1/3RdWOEsmS9v/K38vkTKSWPT+/d828XW96jjY23Hk2nVKeWUjg4sz+TwzcDTkRqLE7OzZs/rQzP9mYEna0Pgm9uBFoORIcWJ2+/Ztvv76a3bv3k1sbKzlWxjDMIiIiOD27dsEBQWlOJCHuT/l0NfX12p/gQIF+OOPP8iZMyfXrl2zOnZ/O1u2bIna8/DwwNXVNck6D05vFBERkZeTf7bMRMbGMXz9n4x+rSpXwsIZsm4LXUoWtSRqyTVwzR+sPXmO7T3akccjvdUxv6yZqJonJ71XrufXN5vi4mDP4HWbKemVlSJZM1uVXXj4ON4Z3KmUOycABTJ6suPCZTK5unDyRij5M3okOnfevHlf+itm586dw8fHR1/ApwGNb9JOnTr1RPVSnJiNGTOGVatWUa1aNc6cOYPZbMbHx4e9e/dy586dVF/V0M/PDzc3Nw4ePEiZMmUs+0+cOEHu3LkpVaoUP//8M/Hx8djb33uj3L59O3nz5k208AeAyWSiVKlS7Nq1i1atWln279y5k9KlS6dq7CIiIvJ8cnd2YmX75gxc8wfeX00hg7MzbYoVZvir5R9bd+v5izSeu5SDvTvh6ujI97sPYm8yUWLSLKtykxq/RttihVnS5nU++P1Pyk6Zw53oGF7Nm4vFbza1Kns3OobPtuxkXaf/fXb5rmFNeixfx5B1m+lVviQlvbImikUflu8xm81JzqSS1KHxtfYk0xjhCRKzP//8kz59+tCzZ09mzpzJzp07GT9+POHh4bRv3/6JM8SHcXFxoVu3bkyaNIls2bJRrFgxVq9ezV9//cVPP/1EgQIF+PHHHxk+fDjdunXj77//ZtasWYwaNcrSxt27d4mNjSVjxowAdOnShR49euDn50e1atVYvHgxQUFBjB49OlVjFxERkeeXX9ZM/Nbx0Ssfvpo3FzEB/a32VcnjTejwvpbtqJHvP7KNDC7OTGr82iPLpHN2Yl+vjonOc/Tdtx5ZT0SeHylelfHOnTuWK0uvvPIKhw8fBsDNzY233nqLP/74I1UDBOjVqxd9+/Zl3LhxNGjQgDVr1jBhwgTKly9PpkyZ+PHHHzl79izNmjVj4sSJDB482HI/GsDo0aNp2bKlZbtKlSqMGTOGBQsW0KxZM3bs2MGUKVNS9d44ERERERGR5ErxFTNPT0/u3r0LQJ48ebhx4wahoaF4enqSLVs2rl69mupBwr2rXF26dEnyWLFixfjll18eWvezzz5LtO/111/n9ddfT63wREREREREnliKr5hVrFiRKVOmcPHiRby9vfHw8GDJkiUAbNq0CU9Pz1QPUkRERERE5EWWrMRs9+7dhIeHA/Dee+9x48YNhg4dislkokePHnz55ZeUK1eOn376iRYt9BR6ERERERGRlEjWVMaOHTvyyy+/UKxYMXLmzMlvv/1meV5Bly5dyJw5M/v27aNYsWJW93a9bEyvFMLInHhFJBEREZH/kskzo61DeCaYTCbMZvMTr5In8l9KVmL24PMvXFxcKFSokGW7cePGNG7cOHUjew451Gxg6xBERERE5P+ZzWb8/Pz+03PGGwb2SgTlCaR48Q95uNHXz3M+NtrWYYiIiIiIDeRxdGZ45jy2DkOeU8lOzI4ePUp0dPKSjrJlyz5xQM+z87HRnIyJtHUYIiIiIiLynEl2YvbvBzY/jGEYmEwmgoKCniooEREREXk5hW7bxZnPvyPi9FnsXFzI2rA2+Yb1w97FhbCgE5z65CvuHjyMndmFbE0bkG9YP+wckv5Ie2nOQi78OIeYkOuYc+Uk7+B3yVyrGgCRFy5x+tOvub17P4ZhkKFMCQp8NAhzrpwY8fEcGzSS6+s24ZQ1CwU/H4lH2ZL36v1zkcM9+lN6+VzsnJ0e2ReTyYSLiwv29vYYhoGTkxPVqlVj4sSJ5MqVy6rskSNH8Pf3p3HjxixfvhyAf/75x2oqZkREBE5OTjj8f3+rVq1KYGDgkw20PHOSnZiNGDGCAgUKpGUsIiIiIvISi7lxk0Nd+vLKpx+QvUVjYq7f4O8OPfln8gxydm7DwXZv4921PcVmTSI6+Bp/d+yJU7Ys5O7RKVFbwb+u4Ny3U/H/cTzpihfl2oo1HOk5gAp/rsY5W1YO93ifdMWKUGHrbxiGwalRX3C423uUXfsrN7ds4/bu/VT4K5CrS1ZxevQ3lF42B4CTAZ+Tf/j7j03K7gsMDKR69eoA3Llzh/bt29O+fXs2b95sVW7ixIl06dKF+fPnc+LECXx9fcmdOzdhYWGWMj4+PgQEBNC5c+cnG2B5piU7MStatCjFihVLy1hERERE5CXmlCkjlfZuxMHdDcMwiA29TUJ0NI6ZMnJ18UrMeXOTp3dXAMy5clJ8zhR4yEIbF6bNJu+AXqQv4Q9Atqb1cc3vg727O7G37+CUJTN5B/TC3tUMgHeXtuyp35rY23cw2dtbtWWyv/eEqZC1G7E3m8lYteIT9S99+vR0796dN99802r/7du3mTt3Lps3byYqKopvvvmGKVOmPNE55PmV4gdMi4iIiIikFQd3NwC2V6zLnrotccqaBa9WTblz4DBuvgU4/sGn/FWmFjuqNeLqstU4e2VL1EZ8ZCThJ05jsrdnf+u32FriVfY170h8RCQObq44ZkhP8dmTcc6axVInJHA9Lt45cMyQHs8qFfCsUoFdNZpyZeEyCnw0iPjISM59M5kCHw184r6FhoayYMGCRM/9nTlzJkWLFqVUqVL07duX2bNnExIS8sTnkeeTEjMREREReeaU/2MFFXeuw2Rnx5GeA4m7fZvgX5eTvkRRKm5fQ9EpX3N5/mIu/DgnUd2423fAMLjww2x8P/2ASrvWk7VpA/7u3JvIC5cSlb80d9G9sp99BIDJzo6CY0dQed8mygYuJH3xopyfMI3srZoSe/MW+1u/xZ5Gbbg0d9Fj+9GoUSM8PDxInz49GTNm5LfffuPtt9+2HDcMg8mTJ9OvXz8AKlSoQLFixZg8efITjpw8r5KVmM2ePZv8+fOndSwiIiIiIgDYu7jgnC0r+Ya+x83Nf2FydCRd8aJ4tX4dO0dH3P0KkrPTm4SsXpeorsnp3v1f3t3a4+ZbADsnR7w7vYlLTi9u/rHVUi4hJpYTI8Zw9quJ+M+YQMYqFZKMJeL0OUK37iBn5zYcHxJAjnatKDH/B86N/57wk6cf2Y9Vq1Zx69Yt7ty5Q0REBB9++CE1atRg3759AKxZs4aTJ0/Ss2dPMmfOTObMmfn777+ZNGkSUVFRTzp88hxKVmJWrlw53Nzc0joWEREREXmJ3d57gJ01XychJtayLyEmFpOTI675fDBiYqzKG/EJYBiJ2nHK6Ilj5oyPLB9zM5QDb3Tlzr6/Kb1yPp6VHv64p5MBn1Hgo0HYOTgQfvw06fz9cEifDnNub8JPPDox+zez2czAgQNJly4d69evB+4t+tGjRw/+/vtvDhw4wIEDBzhy5AixsbHMnj072W3L809TGUVERETkmeBWyJeEqCjOfP4tCTGxRF28zOnR3+DV+nW82rQg7Ngp/pkyEyM+nrBjJ7k0+2eyNWuUZFs52rbk3Hc/cPfIMRLi4rg4cz4xV6+RuU4NEmJj+btjL+zTuVNy8U+Yc+V8aEzXVq7F2Ss7GcrcWy7fnDc3t/ceIDb0FhFn/8GcJ3ey+xcXF8fMmTO5desWVapU4fTp0wQGBtKzZ0+8vb0tP3nz5qVDhw588803GEkknvJiSvaqjCIiIiIiacnBzZVisyZx6uMv2VamJvbp3MnWrCE+fXtg5+xEyV+mc3rsOP6ZPAM7sws52rcmZ+c2ANzatY+/O/em3O9LcMnphU+/d3BI587RvkOIDr6GW4G8+M+ciHP2bISs2UDY4SDsnJ35q1QNqxju1weICwvn/KQfKT7/B8vxVz4exvHBIzk9Zhzend4kXdFCj+xT/fr1sbe3x2QyYTKZ8PX15eeff6ZSpUr079+fYsWKUaJEiUT13n77bSZMmMDKlStp0qTJU46sPA9MRjLS8OXLl1OtWjU8PT3/i5ieO4cOHQJgQmZnTsZE2jgaEREREbGFV5zM/ODla+sw/jMREREEBQVRuHBhXF1dbR3OM+N+buDv75+iesmayhgQEMDZs2cBqFWrFseOHUtheCIiIiIiIvIwyZrK6OTkxPLly4mLi+PSpUscOHCAu3fvPrR82bIPv3lSRERERERErCUrMWvVqhU//vgjCxcuxGQyMWrUqCTLGYaByWQiKCgoVYMUERERERF5kSUrMRs4cCBNmzYlNDSUjh078tFHH1GgQIG0jk1EREREROSlkOxVGV955RUA+vTpQ61atciWLVuaBfW8yuPobOsQRERERMRG9FlQnkaKl8vv06cPMTEx/Pzzz+zcuZM7d+7g6elJmTJlaNasGc7OL+8LcnjmPLYOQURERERsKN4wsDeZbB2GPIdS/IDpO3fu0Lp1awICAjh48CBhYWHs27ePgIAAWrZs+chFQV5kMTExREZqqfy0EBkZydGjRzW+aUTjm7Y0vmlPY5y2NL5pS+ObtmwxvkrK5EmlODH7+uuvCQ4OZu7cuWzcuJFffvmFjRs3MnfuXG7cuMG3336bFnE+F/Rk9rRhGAaRkZEa3zSi8U1bGt+0pzFOWxrftKXxTVsaX3mepDgx27BhA/369aNMmTJW+8uUKcO7777LunXrUi04ERERERGRl0GKE7Pw8HBy5cqV5LFcuXJx69atp41JRERERETkpZLixCxfvnxs2rQpyWMbNmwgTx4tgCEiIiIiIpISKV6VsWvXrvTv35+YmBgaN25M5syZuX79OitXrmTRokUEBASkQZgiIiIiIiIvrhQnZg0aNODcuXNMmTKFRYsWAfdurHRycqJ379688cYbqR6kiIiIiIjIiyzFiRlAr169aN++PQcOHOD27dtkyJCB4sWLkyFDhtSOT0RERERE5IX3RIkZQPr06alWrVpqxiIiIiIiIvJSSvHiHyIiIiIiIpK6lJiJiIiIiIjYmBIzERERERERG0txYjZlyhROnjyZFrGIiIiIiIi8lFKcmP34449cuXIlLWIRERERERF5KaU4MfPx8dEVMxERERERkVSU4uXyq1evzrhx49i0aROvvPIKmTJlsjpuMpno3bt3qgUoIiIiIiLyoktxYjZx4kQA9uzZw549exIdf5kTM5PJZOsQXkgmkwmz2azxTSMa37Sl8RUREZHkSHFiduzYsbSI47nn5OSE2Wy2dRgvJLPZjJ+fn63DeGFpfNPWszq+CUYCdiYtzCsiIvKsSHFi9m93797l2rVr5MqVC3t7e+zt7VMrrufSmvA13Iy/aeswREQeKaN9Ruq51bN1GCIiIvIvT5SY7dy5k6+++orDhw9jMplYtGgR06ZNI3v27AwdOjS1Y3xu3Iy/SUh8iK3DEBERERGR50yK57Fs376drl274uLiwsCBAzEMAwA/Pz9mz57NzJkzUz1IERERERGRF1mKE7Px48dTq1Yt5syZQ6dOnSyJWY8ePejWrRuLFi1K9SBFRJ5VYdfD+LT0p5zceu8xIgv7L2RwrsFWP+9nfp/vW3z/0DY2fLeBkUVGMth7MBMaT+DqyauWY7ev3GZm55l8kP8DPir8EUuHLyU2KhaAhPgE5vacy5DcQxhTfgxndpyx1Lt+7jpfVP2CuOi4x/bBx8cHFxcX3N3dSZcuHW5ubuTIkYNBgwaRkJAAwMyZMylVqhTp0qUjQ4YMVK1aldWrV1u1YxgGP/zwA2XLlsXd3R1PT08qV67M7Nmzkz+gIiIiL6kUJ2ZBQUG0aNECSLwKYeXKlbl06VLqRCYi8ow7s+MM4+uO5/rZ65Z9rb9pzRcXvrD8vDXrLcwZzLz+6etJtrFrwS62TN3CO7++w+hTo8lVPBczO83EMAwSEhL4sf2PxEXHMXz3cAZvHczlI5dZNODeF2DHNh7j7I6zjDw0kspvVWb5iOWWdpcMXULTj5vi4Jy8GetTpkwhLCyMu3fvEh4eztq1a5k1axajRo1i/vz5DBs2jMmTJ3P79m1CQkLo3r07zZs3Z8uWLZY22rVrx9ixYxk+fDhXr17l2rVrDBkyhOHDh9O1a9cnGGEREZGXR4oTs3Tp0hESkvR9VFeuXCFdunRPHZSIyLNu14JdzOkxhwbDGzy0TNiNMOa8PYfmnzXHq7BXkmW2z95Ola5V8CrshaOLI41HNib0Yiintp4i5FQIF/ZfoOWXLXHL6IZ7JncaftiQvb/uJfJOJHb2//8Wfm/igmX779V/4+TqRMEaBZ+4f/7+/lSrVo19+/axdetWihcvToUKFbCzs8PJyYmOHTvy4YcfEhoaCsDy5ctZvHgxGzZs4PXXX8fNzQ1HR0eaNGliSfICAwOfOB4REZEXXYoTs1q1ajFu3DgOHTpk2WcymQgODmbKlClUr149NeMTEXkmFapZiA/3fUip5qUeWmZlwEpylchFmVZlHlom+FgwXn7/S9rsHe3Jkj8Llw5fIiH+3jRCJ1cny3E7OzviY+O5ce4GvtV98a3uy+iyo9k5byfNxjQjJiKGwDGBNBvd7In7Fhsbyx9//MHGjRupU6cOLVu2ZOPGjdSrV4+JEyeyZ88eYmNjGTFiBE2bNgVg6dKlVK5cmXz58iVqz8/Pj0qVKmmqu4iIyCOkeFXGAQMGcPDgQVq3bk3mzJkB6N+/P8HBwXh5edG/f/9UD1JE5FmTPlv6Rx6/cf4Gexbu4f317z+yXHRYtFXiBeBkdiImPIZsvtnIXig7S4cvpflnzUmITWDN52sAiI2Mxc7OjjfGvcEb496w1F31ySrKtytP+M1wZnWbRUxEDBU7VqRyl8qPjKNXr17069fPsu3t7c2AAQPo06cPJpOJvXv3MnnyZMaPH8/p06dxd3enffv2fPnll7i7u3P58mWyZ8/+0PZz5MjB5cuXHxmDiIjIyyzFiVmGDBlYtGgRy5YtY8eOHdy6dYt06dLRoUMHmjdvrocsi4gAO+fuJG/5vHj7ez+ynJOrE7GRsVb7YiJjcHZ3xs7ejm7zu7F02FJGlxlN+qzpqd67Okd/P4rZI/F77dWTVzn+x3H6re3H+Lrjqd6rOn61/RhddjT5K+Yne6GHJ06TJ0+mc+fODz1erFgxpkyZAkBISAjr169n8ODBhIWFMWfOHLy8vDh16tRD6589e/aZfNC2iIjIs+KJnmPm5ORE69atad26dWrHIyLyQji48iA1+tR4bDmvwl5cOXaFInWLABAfG0/I6RC8CnthGAaRtyLpNKMTTuZ7V9WO/n4UZ3dnsuTPkqitJUOW0Gx0M+wd7AkOCiZX8VyY05vJ7JOZ4GPBj0zMHiV37twMGTKE3r17A5AlSxbatGnD9evXmTp1KgCtWrWiefPmHDp0CH9/f6v6+/fvZ9++fYwYMeKJzi8iIvIySPE9ZgCnT59m4MCBVK5cmaJFi/Lqq68yZMgQzp07l8rhiYg8f8JvhnP1xFXyV8r/2LLl25Xnzx/+5NLhS8RGxbJy1ErSZU1H/kr5MZlMzH1nLhvGbyAhIYGQ0yGsDFhJtR7VsHewt2pn35J9eOT0IF+Fe/d4Zc6XmbO7zhJ+M5yQ0yFkzpf5ifvTvn17Pv30U1atWsXt27eJi4vj4MGDTJ8+3bJKb6NGjWjXrh2NGzdmxYoVhIeHEx4ezvLly2natClt2rShUaNGTxyDiIjIiy7FV8y2b99O9+7d8fT0pHr16mTKlImQkBA2b97M+vXrmTdvHoUKFUqLWEVEngs3zt8AIINXhkTHTm8/zdTWUxm2fRie3p6Ub1+eyNuRzOgwg7AbYeQumZseP/fA3vFe4tV5RmcWDVrEH9//gUs6Fyq0r0DdIXWt2oy6G8Xv3/xO7+W9LftaftmSBX0XsOKjFVTtXhXvYo+eUvkoo0ePJkeOHAQEBHD8+HESEhLImzcv3bp147333rOUmzlzJjNmzGDs2LF07NgRuLfwx6hRox45TVJERETAZNx/QnQytW7dGjc3N6ZOnYqT0/9uWA8PD6dbt244Ojq+dA8Tvb9C5aE8hwiJT/pRAiIiz4os9llom76trcNINREREQQFBVG4cGFcXV1tHc4LR+ObtjS+aUvjm7Y0vkm7nxs8OLX/cVI8lfHYsWN07tzZKikDcHNzo0ePHhw8eDClTYqIiIiIiLzUUpyYeXl5PXTJ4/DwcMsS+iIiIiIiIpI8KU7MBg0axPjx4wkMDCQ+Pt6yf+fOnXzzzTcMHDgwVQMUERERERF50SVr8Y9ChQphMpks24Zh0L9/f+zt7fHw8ODu3bvExMRgb2/P6NGjqV+/fpoFLCIiIiIi8qJJVmLWu3dvq8RMkubj6IOnnaetwxAReaQM9olXi3yemUwmzGaz/p1KIxpfEZH/RrISs759+6Z1HC+ESuZKtg5BROSlYzab8fPzs3UYL6zUHF8jIQGT3RM9QlVE5IWX4ueYAcTExHDmzBnu3r2b5PGyZcs+VVDPq7gl8zBCrto6DBERkWeOKUs2HJq3s3UYIiLPrCd6wPSAAQMIDQ0F7t1vBvemOhiGgclkIigoKHWjfE4YIVch+JKtwxAREXnmpOihqSIiL6EUJ2ZjxozB09OTgIAAPDw80iAkazt37qRjx45JHvP29mbDhg0EBQUxevRoDh8+jIeHBx06dKBr166PbDcwMJAJEyZw4cIFfHx8GDRoENWqVUuLLoiIiDy1g8EhDF23mX2Xr+Jkb89r+fPwZd3qZHYz83dwCIPW/sHuS8G4OjrSxr8QY2tXw8E+6WmDX23dzaSd+wmNiqJMjuxMavwaBTNnBODAlWsMXruZfVeu4mhnR91X8vJ1vepkcjUTn5BAt2VrWXHsNF7p3JjapA6V8+QE4MzNW7T6ZQXburfF2eGJJuSIiLzUUjzR+59//mHAgAHUqVOHcuXKJfmTmkqWLMnWrVutfmbMmIGDgwPvvPMOoaGhdOnSBR8fHxYvXkzfvn359ttvWbx48UPb3LFjB4MGDaJt27YsW7aMKlWq0Lt3b06fPp2qsYuIiKSGyNhYmsxdQoVcObgw8B0O9O7Ezcgoui1fy/XwSOrN/pWa+fJwdUgvtnZrw+oTZ/hux74k25p94AiTdu5nVYfmBA/uRakc2Xjjl5UYhkFMXDxN5y3l1by5CB7ci6B33yL4bjiD1m4GYN2pc2z75zKn3+/GO2WLM2TdZku7/QM38XmdV5WUiYg8oRS/exYsWNAyjfG/4OTkRJYsWSzbsbGxjB07ljp16tCqVSumTp2Kk5MTAQEBODg4kD9/fs6fP8+0adNo0aJFkm1OmzaN2rVr0759ewCGDBnC/v37mTVrFh9//PF/0i8REZHk+uf2XYplz8KHr1bA3s6OTA5mupUuRpelgcw5eIRXMnkypOq9L0Z9PDMQ2LElD1tDccbeQ7xdtjhFsmYGYPRrVZi+9xCbz12get7cHH33LcwODtjZmQiNiiY8NpYsrmYAHP5/4Y770xLt/397edApXJ0ceS1/njQbAxGRF12KE7MPPviAgQMHYmdnR7FixTCbzYnK5MiRI1WCS8q8efO4cuUKM2bMAGDPnj2ULVsWh399Q1ehQgWmTp3KjRs3yJQpk1X9hIQE9u3bx9ChQ632ly9fnt9//z3N4hYREXlSBTNnZGX75lb7lhw9QSmvbOy+FEyRrJnovXI9K46dws3JkU4lizKkStIzWI6G3GBglf8t0uVob0+BTB78HXyd6nlz4+bkCMCr039m+4XLFM6Sif6VywBQK18eauXPTZEJM8mRzp0pTWoTERNLwKa/WN0+6S9DRUQkeZ54VcYPPvjgocfTavGP6OhopkyZQqdOnciaNSsAwcHB+Pr6WpW7f+zy5cuJErM7d+4QERFB9uzZE9W5cuVKmsQtIiKSWgzDYOTGbaw+cYYNXVozeO1mlgedYlKj1xjfoAZHQ27SfP4ynO3tLQnVv92NjsHN0dFqn6ujI2ExMVb71nRsQVRcPH1WbaDe7F/Z804H7O3smNy4NpMb17aU+3D9VjqXLMr1iEja/7qa8NhYupbyp0fZ4mkzACIiL6gUJ2YBAQHY29vz/vvvW00x/C8sX76c6OhoOnToYNkXFRWFk5OTVTlnZ2fgXiL3oKioKIAk6yRVXkRE5FlxJyqabsvXsv/yNTZ0aY1/tiw429tTNmd2OpcqCkDx7FnoVb4Evx45nmRi5ubkSERsnNW+iNhY0jlb/7todnTE7OjIuPo18P5qCn9fvU5Jr6xWZY5fv8mGM+f5s2sbqk5fwHsVS1P/lbwUmTCTKnm88ctq/eUoQGRkpGVFZ7k3Hv/+r6QujW/a0vgm7f5K9SmV4sTszJkzfPvtt9SoUSPFJ3tay5Yto06dOnh6elr2ubi4EPPAt3z3EyxXV9dEbdxP2pKqk9S0TBERkWfB6Zu3aDJvKbkzpGN7j3Zkdrv3b1ahLJnYfO6CVdn4BOOhy9MXyZqZoyHXaVgwHwCx8fGcunGLIlkzcy70NnVmLWJz1zfxSucOQHR8PAAZzS6J2nr/t018Xa86DvZ2HLl2nVJe2cjg4kw+zwwcDbmRZGJ29uxZfYhLwrlz52wdwgtN45u2NL6JPXgRKDlSnJjlyZPHJm+oN2/eZP/+/bz99ttW+7Nnz861a9es9t3fzpYtW6J2PDw8cHV1TbLOg9MbRUREngWhkVHUnbWI6nlz80OTOtjZ/e+b2M4lizJp536+2rqb9yuV5mjIDb7fdYABSVwtu1e+CB9v2k6dAnkpmMmTjzb+RTZ3V6rmyYmDnR2eZhcGrtnM1Ca1iYqLp+/qDdQr4EMej/RW7Sw8fBzvDO5Uyn1vufwCGT3ZceEymVxdOHkjlPwZPZI8f968eXXF7F8iIyM5d+4cPj4++oI4DWh805bGN2mnTp16onopTszee+89Pv/8czJkyECJEiVwc3N7ohOn1L59+zCZTImW4y9btiw///wz8fHx2NvbA/cegp03b95E95fBvQdhlypVil27dtGqVSvL/p07d1K6dOm07YSIiMgTmLX/CP/cvsuvR46z+MgJq2Ohw/uyoUtrhq7bwhdbd+Hq6ECPMsXpXb4kAFvPX6Tx3KUc7N2J3B7p6VyyKLeiomn98wpCIiIokyM7y9o2w/H//w1d/GZT+q/ZRIHxP+LiYE+TQgX4pFYVq3PejY7hsy07Wdfpf/+OftewJj2Wr2PIus30Kl8y0bTH+/ThLWlmsznJmT6SOjS+aUvja+1JpjECmIwUfm1Vv359goODLfdqJRXI0aNHnyiYR5k4cSIrV65k7dq1Vvtv3LhB/fr1qVmzJt26dePvv/8mICCAUaNG0axZMwDu3r1L7P+1d99hUVxfA8e/y9KLCDZUFKwooogNFXuJBVssiS3GGDWKvWuwkYhYsGJXrIk1YoslQU2wxIhd7IqKXewoHXbfP/gxrxtQUcFVPJ/n4ZGduXPnzGWEPTu3JCZia5uyeOaBAwfo2bOnsqj0xo0b+fXXXwkKCqJYsWJvHVtYWBgApf4Jhnu33/NKhRBCiGzIriBGPwzWdxQfnZiYGM6fP0/p0qXljW0WkPbNWtK+6UvNDcqWLftWx731EzNPT8+3PSRTPHz4kJw5c6bZnitXLpYsWYKvry9ffvklefLkYfjw4UpSBuDr60toaCh79+4FoEaNGkycOJF58+YxY8YMihcvzoIFC94pKRNCCCGEEEKI9/XWT8xEWvLETAghhHgDeWKWLnnikLWkfbOWtG/6PtgTszt37ryxTFYuMC2EEEIIIYQQ2c1bJ2b16tV744C2rFpgWgghhBBCCCGyo7dOzCZOnJgmMYuJieHYsWP8+++/TJw4MdOC+9SoSpRCmzv9WaiEEEKIz5nKxlbfIXyUVCoVZmZm7zyLm3g9aV/xKcnUMWaTJ08mMjKSadOmZVaVn4R37UcqhBBCCCE+PclaLWpJ9mSM2St8sDFmr1OnTh28vLwys8pPiu/DCCIS4/UdhhBCCCGEyCIORiZ453bQdxgiG8rUxOzkyZMYGmZqlZ+UiMR4LifE6jsMIYQQQgghxCfmrbOoUaNGpdmm0Wi4e/cuR48epW3btpkSmBBCCCGEyJ6e/BPK1cmziQm/hoGpKXk9G1J01EDUpqa8OH+JKz/78/zUGQzMTMnXsilFRw3EIJ0P/5Pj4rk6eRYPdu4mOToG86KOFB0xAJvqlVP2x8RyeawfD3eHoE1OJnfDOpT4+UcMLczRJidzYdg4Hv75F8Z58+A0eRw5K7sBEHvjFmd6Dqbill8wMDH+oG0jPl8Gb3vA4cOH03wdPXqUZ8+e0aNHj3QTNyGEEEIIIQASHj0m7Lt+FOjcjhqn91Npx1qe/nuUG/OWkvD4Cac6/YCNhzseJ0OosGkVj/bu49bSX9Ot6+rkWUQdO0mFoJXUOBlC/vZfEvZ9P+Ju3wXg8lg/4u7ex/3vrbj/tZW423e5OmkmAI/3/cOzIyeoenAnBb/5inDf6Uq9l8dPppj3oAwlZU+ePMHLy4tChQphYWFB/vz5+fbbb7l165ZS5tGjRwwePJjixYtjaWlJgQIF6NSpE2fOnNGpKz4+nlGjRlGsWDEsLS3JkycPbdq0SXfG80ePHmFubk758uXfGKP4NLx1YrZ37940X3v27GHLli0MGjQIMzOzrIhTCCGEEEJkA8a5bKl+bC/527UElYrEJ8/QxMdjlMuW+xu3YVakMA59vsfAyAizQgVxXbWAvJ5fpFuXJi4ex0FemBawQ6VWU6BDGwyMjXkedp7k2Fjub9lBkcG9McppjXFuW4qNHMi9DVtJjo1FpVbr1KVSp7wtfvDHXtRmZtjWrJah6/n66695+PAhR44cITo6mpMnTxIfH0/Dhg1JSkrizp07uLq6cuPGDbZt20ZUVBQnT56kaNGiuLu7ExwcrNTVr18//vnnH/bs2cOLFy+4fPkyhQoVolatWjx9+lTnvEuWLKFJkybcvXtXpw7x6fp8B4QJIYQQQgi9MLS0AOBQtUYk3IvEukoF8rdryYVh47AoWZyLP07g4Z9/oTY3I/9XLSns9X269Tj5jdF5/eSfUJKev8CyjBOx126gTUzCwqmEst+8RFE0cXHEXI3ApkZVbGpUJbRuS4zz5cFp0liSY2O5Pn0e5VbOy/C1HDhwgMDAQOzs7ADIly8fM2fOZOTIkTx58oTBgwfj4ODA+vXrMTBISf7y5s3Lzz//TFJSEl27diUiIgJDQ0MOHDjAN998g6OjIwA5c+Zk6tSpREVFce/ePXLmzAmkDCOaP38+06dPp1SpUkybNo2GDRtmOGbxccpQYvY23RNVKtVnvZaZEEIIIYTIGPe/t5L0LIrzA37kbO+haJOTePjnXkr6jqaEzwhiLl8lrPsAVMbGFO757Wvrenb8NGe9huE4sBdmhQry9MgJANTm/9+bS21mCqSMPVMZGODkN0Ynubs6ZTZ27VqS+Pgp5/qNJDkmlvztW1Owc7tXnrdDhw706tWL/fv3U6dOHdzd3XFwcGD58uUkJSWxadMmZs+erSRlL+vRoweTJk3in3/+oVatWnTo0AEfHx8uXrxI/fr1cXd3p2TJkixdulTnuK1bt5KcnEyLFi2oUqUKRYsWJSwsTJZu+sRlKDE7fPjwG8s8efKE2NhYScyEEEIIIUSGqE1NUZuaUnTkAI63+gbbujWwcnUh/1etALB0dqLgt+15sP3P1yZmd9YGceWnqRQZ7EWh7t+k1P2/4TXJsXEYWpgr3wPK65fFhF/nyYF/cQtayYnWXbD//hty1a3B4XotyeleAYsSxdI99+LFi6lbty5r166lZ8+ePHv2jGLFiuHj40P9+vVJSEigcOHC6R5rb28PwO3btwEYM2YM5cuXZ+XKlQwZMoQHDx5QoEABhg4dyqBBg5Tj5syZQ9++fTE0NMTe3p7WrVszffp0li1b9so2Eh+/DCVme/fufeW+xMRE5s+fz6JFi8idOzfjx4/PrNiEEEIIIUQ28+zYSS4MG0/lXRswMDYCQJOQiMrYCPOijjw7clynvDZZA1ptunVpk5O5NHoiD//Yi8uiGdjWqKrsMy/mgMrIkJhL4eRwS3mSFHP5KipjI8yKpl2H7PL4SRQfOwwDQ0OiL4ZjVdYZwxxWmBW2J/pS+CsTMwMDAzp37kznzp3RarWcP3+eVatW8c0337Bz506MjIyIiIhI99g7d+4AKd0fUzVv3pzmzZsDEB4eTlBQECNHjiRHjhx8//33nD9/nj179nD06FGmTp0KQFxcHImJiUycOJH8+fOney7x8XvryT9edv78edq2bcv8+fNp3Lgx27dvp0GDBpkVmxBCCCGEyGYsSpVEExfH1cmz0CQkEnfrDuG+08n/VSvyd2jDiwtXuLFgGdrkZF5cuMztlWvJ92WzdOu68rM/j0MOUnHrrzpJGaQ8McvbrBFXJ88i4dFjEh495urkWeRt3hi1qalO2chtf2CS3w7rSinT5ZsVKcyzYydJfPKUmGs3MHNI/4nXH3/8gaWlJY8fPwZShvQ4Ozvj5+eHm5sbYWFhfPnllyxduhSNRgPA48ePmTt3LtHR0QQGBpIvXz48PDw4f/48pqamOjM1FitWjGHDhtGsWTNOnEjpmjlnzhyaNm3KmTNnOHnyJCdPnuTChQsULVqUgICAd/iJiI+FSqt9xUcQr5GUlMTcuXNZvHgxOXPmVB7Vfq7CwsIACMhtIgtMCyGEEEK8QfTlcK78NJXnp86itrIk35eeOPbriYGJMVEnwgj3m0H0hcsYmJlSoPNXOPTtjkql4mnocU537UOV4CAMzEz5p1J9VGoDVEZGOvU7TRxNvlaeJL2IJtx3Gg+DQ9AmJqasY/bTKJ1xZ0kvojnRtiuuqxdhbGsDwNPQ41wcPo7EZ8+x/7Y9jgN7KeVLGJuxKH9JAGJjY3FxccHFxYUJEyZQqlQp4uLi2Lp1K7179+bw4cPkzJkTd3d33Nzc8PPzw9LSku+++44LFy7w4MEDNm/eTNOmTdFqtdSoUQOAGTNmUK5cOTQaDSEhIXTq1IkNGzZQuXJlChYsyKpVq2jVqpXONU+fPp0JEyZw8+ZNLCwssuLHlkZMTAznz5+ndOnSmJun7R76uUrNDd52zN9bJ2bnzp1j1KhRXLx4kRYtWjB69Ghy5MjxVifNbiQxE0IIIYT4PLycmAHcvXuX8ePH88cffxAZGYmxsTHVqlVj/PjxuLu7Aylrjk2YMIEtW7Zw//59rKysqF27Nnfu3MHY2JipU6dSoUIFnj17ppS7ffs2arWa8uXLM2rUKJo0acLs2bOZMGECt2/fxug/yeijR48oWLAgU6ZMoX///h+kLSQxS1+WJ2ZJSUnMmTOHJUuWYGtri4+PD3Xr1n37SLMhScyEEEIIIT4P/03M3odWq2X79u0UKVKEMmXKZEqdH5IkZul718QsQ5N/nD17lpEjR3LlyhVatWqFt7c3lpaWbx+lEEIIIYQQAkgZk9asWfrj58TnJ0OJ2VdffYVGo8HKyorbt2/j5eX1yrIqlYoVK1ZkWoBCCCGEEEIIkd1lKDGrUKGC8v2bej6+w1wiQgghhBBCCPFZy1BitmrVqqyOI1twMDLRdwhCCCGEECILyfs9kVUylJiJjPHOnXaxQiGEEEIIkb0ka7WoVSp9hyGymfdaYFr8v4SEBGJjZUbGrBAbG8u5c+ekfbOItG/WkvbNetLGWUvaN2tJ+2atrGpfScpEVpDELBPJ+LqsodVqiY2NlfbNItK+WUvaN+tJG2ctad+sJe2btaR9xadEEjMhhBBCCCGE0DNJzIQQQgghhBBCzyQxE0IIIYQQQgg9k8RMCCGEEEIIIfRMEjMhhBBCCCGE0DNJzIQQQgghhBBCzyQxE0IIIYQQQgg9k8RMCCGEEEIIIfRMEjMhhBBCCCGE0DNJzIQQQgghhBBCzyQxE0IIIYQQQgg9k8RMCCGEEEIIIfRMEjMhhBBCCCGE0DNJzIQQQgghhBBCzyQxy0QqlUrfIWRLKpUKMzMzad8sIu0rhBBCCKF/hvoOILswNjbGzMxM32FkS2ZmZjg7O+s7jGzrU2pfjVaDgUo+TxJCCCFE9iOJWSbaFb2Lx8mP9R2GENmSrdqWxhaN9R2GEEIIIUSWkMQsEz1OfsyD5Af6DkMIIYQQQgjxiZE+QUIIIYQQQgihZ5KYCfEZevHwBRMqTuDygcvKtlNbTzGl1hRGFB6Bj6sPuybvQqPRpHt8zNMYVv2wCu/i3owoPIK5reZyK+yWsv/Z3Wcs67qMH4v9yNjSY9nkvYnEuEQANMkafun9CyMKj2Ci+0Su/ntVOe7h9YdMqTmFpPikN16Do6MjpqamWFpaYmVlhYWFBQUKFGDYsGFK3MuWLaNChQpYWVlhbW1NzZo12b59u049Wq2WRYsWUblyZSwtLbGxscHDw4OVK1dmvEGFEEIIId6TJGZCfGau/nuVmY1m8vDaQ2XbzZM3+aX3L3h6e+J33Y8f1v9A6JpQQuaFpFvH2v5riXseh/cxbyaGT6RwhcIEdgoEQKPRsKTzEpLik/A+4s3wA8O5c/YOG4ZsAODC3gtc+/ca48LG4dHNgy1jtij1Bo0MouVPLTE0yVgv6wULFvDixQueP39OdHQ0f/zxBytWrMDHx4fVq1czatQo5s2bx7Nnz3jw4AE9evSgdevW7Nu3T6mjU6dO+Pn54e3tzf3794mMjGTEiBF4e3vz/fffv3X7CiGEEEK8CxljJsRnJHRNKDv9dtJ8fHNWdv//J0KPbzymetfqlGlUBgA7JzvKepYl/FA4dfvWTVPPt4HfoknWYGRqRMzTGGKfxWKZ2xKAB1cecPPETcadHoeFrQUAnqM9CfAM4Eu/LzFQ/+/zIG3KP6mvT28/jbG5MU51nd75+sqWLUutWrU4fvw4Dx48wNXVlapVqwIpM6d26dKFiIgInjx5AsCWLVvYuHEj58+fp2jRoko9LVq0oHjx4pQrV462bdvSpEmTd45JCCGEECIjJDET4jNSql4pKrariNpQrZOYubZwxbWFq/I6ITaBc8HnqNi2Yrr1qI3UqI3UbJ+wnd0zdmNiaULPdT2BlK6KAMbmxkp5AwMDkhOTeXT9ESXrlKRknZL4VvbFOr817We2JyEmgZ0Td9Lrt17vfG2JiYkcPHiQvXv34uPjQ5kyZWjUqBGNGzemWbNmVK1aFVdXV8aMGaMcs2nTJjw8PHSSslTOzs5Ur16dDRs2SGImhBBCiCwnXRmF+IzkyJcDtaH6tWXinscR2DkQI1Mj6vSu89qyDYc0ZOqdqTQa3ogF7Rbw8PpD8pXMh10pOzZ5byLmWQwvHr5g1+RdACTGJmJgYMDXM77G97Ivw/cNp3CFwvw57U/cO7kT/Tia2Z6z8a/rz8FlB994PV5eXuTMmZOcOXOSJ08e+vbty5AhQ+jbty/16tXj2LFjODo6MnPmTCpXroytrS29e/fmxYsXANy5cwc7O7tX1l+gQAHu3LnzxjiEEEIIId6XJGZCCMX9y/eZ2WgmmmQNfbf2xdTK9LXljc2MMTQxpG6futgUtOHMjjMYqA3ovro7sU9j8a3ky9yWc3FtmfI0zixn2kXY71++z8W/L1KzZ03W9F+DRzcP+mzpw85JO7l34d5rzz9v3jyePn2qfJ05cwZvb29UKhUA5cqVY8GCBVy5coXIyEgWLVrE77//Tu/evQHInz8/ERERr6z/2rVr5M+f/7UxCCGEEEJkBknMhBAAnAs+x4wGMyhdvzS9fuuFeU7zV5ad2WgmJ7ec1NmWlJCEuY05Wq2W2KexfLv0W3wv+zLi4Ais8lhhYmlCnmJ50tQVNCKIL32/RG2o5t75exRyLYRZDjNyO+Z+Y2L2OoULF2bu3LnK6zx58tChQweGDx/OiRMnAGjXrh1HjhwhLCwszfEnTpzg+PHjtGnT5p1jEEIIIYTIKEnMhBBcP3KdwG8CaeXbipY/t3xjd0eHig7smrSLxzcfkxSfxE6/nSTFJ+HSxAWVSsUvvX5hz8w9aDQaHoQ/YNv4bdTqWStNvceDjpOzYE6KVk0Z45W7aG6uhV4j+nE0D8IfkLto7ne+ps6dOzNhwgR+//13nj17RlJSEqdOnSIwMFBJtpo1a0anTp1o3rw5W7duJTo6mujoaLZs2ULLli3p0KEDzZo1e+cYhBBCCCEySib/EEIQPCMYTaKGoFFBBI0KUrYXrVqUXht6EX4onIVfLWTUoVHY2NvQfFxzDNQGzGw0k+SEZBwqOdBnSx/lKVvXpV3ZMGwDf8//G1MrU6p2rkqjEY10zhn3PI7g6cH02dJH2dZ2alvW9FvD1rFbqdmjJvbl7N/5mnx9fSlQoADjx4/n4sWLaDQaihQpQvfu3RkwYIBSbtmyZSxduhQ/Pz+6dOkCpEz84ePjQ9euXd/5/EIIIYQQb0Ol1Wq1+g7iU5faDSrMIYwHyQ/0HI0Q2VMedR465uio7zDeWkxMDOfPn6d06dKYm7+6e6h4d9LGWUvaN2tJ+2Ytad+sJe2bvtTcoGzZsm91nHRlFEIIIYQQQgg9k8RMCCGEEEIIIfRMEjMhhBBCCCGE0DNJzIQQQgghhBBCz2RWxkzkaOSIjYGNvsMQIluyVlvrO4R3olKpMDMzUxa9FkIIIYRIjyRmmai6WXV9hyCE+MiYmZnh7Oz8wc+r1WhQGUinCCGEEOJTIYlZJkoK+hXtg/v6DkMI8ZlT5cmHYetO+g5DCCGEEG9BErNMpH1wH+7d1ncYQojPnCxOKYQQQnx6Pqp+LvPmzeObb77R2Xb+/Hk6d+5M+fLlqVOnDoGBgTr7NRoNs2fPpmbNmri6utKtWzciIiJee54nT54wZMgQKleuTOXKlRkzZgwxMTGZfj1CiE/bg+gYSs8KJOTaTWXb6XsPaLRiA7YTA7CfuoBhu/4mKVnzyjoWHjlF6VmB2PgG4DZvBdsvXlX2RSck0n3zH9hNnkduvzl8F7STF/EJACRrNHwXtJNcE+fgErCMgxH//6HP1cdPqTh/JfFJSVlw1UIIIYTQh48mMVu+fDmzZ8/W2fbkyRO+++47HB0d2bhxI/369WPWrFls3LhRKTNv3jzWrl3LhAkTWLduHSqVih49epCQkPDKc/Xv35+bN28q5zx48CA+Pj5Zdm1CiE/PPzduU2vJGsKfPFO2PYyOpfHK36hX1IH7I7w40L0D2y9dZfa/x9OtY+XJs0z4+xAr2zTl8Y99GVHTna/Xb+NO1AsABuzYy61nzznX7zvO9evGzWfP+XH3fgD+vHKdf27cIXxQd3pVdmXEnyFKvYN3/sXkL2pjYiidHoQQQojsQu+J2f379+nevTuzZs2iSJEiOvvWr1+PsbEx48ePp1ixYrRp04auXbuyePFiABISEli6dCn9+vWjdu3alCpVihkzZnD//n2Cg4PTPd+JEycIDQ3Fz8+PMmXKUK1aNX766Se2bNnC/fsyPkwIkZJQddm4A5/6NXS2rzp1lhK5bBhRswpGajWONtbs7NKWtmVKplvPjH+OMr5edSrb50elUtG+bCn2f9+eHCbGxCQksub0ecbVrY6tuRl5Lc2Z2LAmK06cJSYhEcP/TdyR2i1R/b/XW85fwdzYiAbFHLLs+oUQQgjx4ek9MTt79izW1tZs3boVV1dXnX1Hjx6lcuXKGL70qXDVqlW5du0ajx494sKFC0RHR1O1alVlf44cOXB2dubIkSPpnu/o0aPkyZOHYsWKKduqVKmCSqXi2LFjmXx1QohP0RfFHLnQ/3u+cnHS2X7k9j3K5M1Fn227KTR1AaVmBbL69Hnsc1ilqSMmIZFzkY9Qqwyot3QddpPnUWvJGqITE7E0Meby46ckajS45MutHFM6Ty5ik5K49OgJ9Ys6UL9YYcoELGP5ibNMa1yHmIRExv91EP9GdbK6CYQQQgjxgem9H0y9evWoV69euvvu3btHyZK6n0TnzZsXgDt37nDv3j0A8ufPn6bM3bt3063z/v37acobGxuTM2fOVx4jhPi82FlZpLv9SWwcW85fYW6zBsxsWpdzDx7TevVmTNRqBntU0i0bF4cWmP7PUdZ81YwStjYsOXaa5r9s4oRXF2UsmYWRkXKMuVHKr+TohEQMDFTMa96Qec0bKvtH7z5AVzcXHsbE0vm37UQnJvJ9hbL0rKz7oZYQQgghPj16T8xeJy4uDmNjY51tJiYmAMTHxxMbGwuQbplnz56RntjY2DTlU4+Jj4/PjLCFENmUiVpN5YJ2dK3gAoCrXR683Mvz29mLaRIzE3XKr9eB1SpSJm/KUzEvdzcWHj3NrsvXcC9UAICY/z1BS/k+ZTKP1Ncvu/jwMXuuRrD/+w7UDFzDgGoVaVKiCGUCllHDwR7nvLnSHBMbG4tWm/3naEz9W5D6r8hc0r5ZS9o3a0n7Zi1p3/RptVpUKtVbH/dRJ2ampqZpJvFITZ7Mzc0xNTUFUsaapX6fWsbMzCzDdaYeY25unlmhCyGyoVJ5chFy/abOtmSNNt3p6XNbmJHXwpz45OT/lNegBZxy2WBkYMC5B4+oYp/yFP/8g0cYq9WUzJUzTX2DdvzFtMZ1MFQbcDbyIRXy58Pa1ISiNtace/Ao3cTs2rVrn9Ufy+vXr+s7hGxN2jdrSftmLWnfrCXtm1Z6D4Le5KNOzOzs7IiMjNTZlvo6X758JP1vqujIyEgKFy6sU6ZUqVKvrHP37t062xISEnj69Cn58uXLzPCFENlMVzcX5h4+gf+BIwyqXpFzDx4xP/QkQ/7ztCxVj0rl8A35l2qFCuCSNzcLjpzkzvMXtChVDHNjI9q5OOG9ez+r2zUDwHv3fr52ccLspe6NAOvPXMTe2pLqhQsCUNzWhn9v3iGXuSmXHz2hmG3OdM9fpEiRz+aJ2fXr13F0dHzlh3Li3Un7Zi1p36wl7Zu1pH3Td+XKlXc67qNOzCpXrszatWtJTk5GrVYDcOjQIYoUKUKuXLmwsrLC0tKSw4cPK4lZVFQU586do3Pnzq+s09/fn4iICBwcUmY1O3z4MAAVKlT4AFclhPhUlcpjy57vvmLkn/uYciAUcyNDelZypY+7GwAHIm7R/JdNnOrzLYVz5mBM7WrkMDGm02/buRP1glJ5bNna6UsK/m+ykADP+gz/I4QK81aSkJxM81LFmdVUd8zt8/gEJu07zJ/ftlO2zfasR88tfzLizxC83N1wy5833Xg/tz+SZmZm0vMhC0n7Zi1p36wl7Zu1pH11vUs3RvjIE7M2bdqwZMkSvL296d69O6dPn2bFihXKmmPGxsZ07twZf39/bG1tKViwIFOnTsXOzo6GDVMGzCcnJ/P48WOsrKwwNTXF1dWVChUqMGjQIMaPH09MTAzjxo2jVatW8sRMCJFGwvjBOq+r2Odnb7ev0y1bw8GeJ979lNcGBioGVa/EoOrpP1GzMjFmfouGzKdhuvtTyxz36pLmPOf6d8voJQghhBDiE6D36fJfJ1euXCxZsoRr167x5ZdfMmfOHIYPH86XX36plOnfvz9t27Zl9OjRdOjQAbVaTWBgoNKv8+7du9SoUYMdO3YAKRnsnDlzsLe359tvv2XgwIHUqlWL8ePH6+MShRBCCCGEEOLjemI2adKkNNvKlSvHunXrXnmMWq1m2LBhDBs2LN399vb2XLx4UWdbrly5mD179vsFK4QQQgghhBCZ5KN+YiaEEEIIIYQQnwNJzIQQQgghhBBCzz6qroyfOlWJUmhzpz87mhBCfCgqG1t9h/BBqVQqzMzM3nkWLCGEEOJjIIlZJjKs11TfIQghxGfHzMwMZ2fnLKk7WatFLQmfEEKID0ASs0zk+zCCiMR4fYchhBAiEzgYmeCd20HfYQghhPhMSGKWiSIS47mcEKvvMIQQQgghhBCfGJn8QwghRJZ4ce4ipzr/wAHXWhysVJ/zg0eT8PiJTplnx04RUrLKa+tJjovnss8U/qn6BfvL1uBYy848+eeI7v7xkzlYqT77y9bgZMeeRF+5BoA2OZnzg0ez38WDw/Va8fTICeW42Bu3ONL4KzTxCRm6HkdHR0xNTbG0tNT5UqlUmJiYEBMTo5SNi4vD0tKSggULotVqle1Xr15FpVJx5swZZdvcuXNRqVTMmDEjQ3EIIYTIniQxE0IIkemS4+I43bUvOSq4Uv3IHqoEbyTxyVMuDhsHgFar5e76zZzu0httwusTo6uTZxF17CQVglZS42QI+dt/Sdj3/Yi7fReAy6N9eX7mPJV2rMXj6F7MixfhrNdQAB7v+4dnR05Q9eBOCn7zFeG+05V6L4+fTDHvQRiYGGf4uhYsWMCLFy/SfKlUKg4cOKCU2717N46Ojrx48YLDhw8r24ODg3F0dMTFxUXZNnfuXHr37s2sWbNISkrKcCxCCCGyF0nMhBBCZLr42/ewLF0SxwE/YGBshJFNTgp0bMvT0OMAXBw2jrtrgnAc1PuNdWni4nEc5IVpATtUajUFOrTBwNiY52HnSXj4mHubtlNqqg8mefNgYGJMsZEDKT19AlqtFpVarVOXSp3yZ+/BH3tRm5lhW7Pae1+rhYUFderUYe/evcq2zZs306xZMxo3bszmzZuV7bt376Z58+bK6z179hAZGcm0adPQaDT89ttv7x2PEEKIT5MkZkIIITKdeTFHyq2Yq5MYPdi5GyuX0gA4DulDhU0rsSxT6o11OfmNIVfdGsrrJ/+EkvT8BZZlnHgedg7DHFZEnQgjtGFrDlasy/lB3hjZ5ESlUmFToyo2NaoSWrcld9dvpvjYYSTHxnJ9+jyKjx2aadfr6empJGYajYZt27bRsmVLWrRooSRmGo2GvXv30qJFC+W4gIAAevTogZmZGV5eXkybNi3TYhJCCPFpkcRMCCFEltJqtVz1n8Oj3SEUHzccANP8+d6prmfHT3PWaxiOA3thVqggSc+ekRT1nAc7d1N+7RLc/9qK2tyMsO8HoE1ORmVggJPfGDyO/0XlnevJ4epCRMBi7Nq1JPHxU0581Y2jzTpw+5cNGTq/l5cXOXPm1PmKjo7G09OT48eP8+zZM/755x9UKhXu7u40bdqU8PBwLl68yPHjx0lKSqJ27doAREREsGvXLvr06QNAz549OXv2LCEhIe/UNkIIIT5tMiujEEKILJP0/AUXho3jedg5yq9fimWpEu9c1521QVz5aSpFBntRqPs3AKiMjSE5mWLegzHOlbKwdrHRQ/inYj1irl7HokQxnTpiwq/z5MC/uAWt5ETrLth//w256tbgcL2W5HSvkKb8f82bN4+uXbum2V60aFFKlChBSEgI+/bto3nz5hgYGGBjY0PNmjXZuXMnsbGxNGrUCCMjI6WuxMREypcvr9STmJiIv7+/krwJIYT4fEhiJoQQIkvERtzk9Hd9MS1gR8VtqzG2tXmnerTJyVwaPZGHf+zFZdEMbGtUVfZZlCiaUualCUS0yZr/faPlvy6Pn0TxscMwMDQk+mI4VmWdMcxhhVlhe6Ivhb8xMXsdT09P9u/fz/bt25k6daqyvUWLFuzZs4eYmBi+++47IGXWxsDAQAIDA2nQoIFS9syZMzRt2pQLFy5QqtSbu3kKIYTIPqQroxBCiEyX+CyKkx17Yl3BlXIr579zUgZw5Wd/HoccpOLWX3WSMgCLEsWwrlKRiz9OIOHxE5KiYwj3nYalS2ksShbXKRu57Q9M8tthXckNALMihXl27CSJT54Sc+0GZg6F3zlGSEnMgoKCuHnzpk6y1bJlS0JDQwkNDaVJkyYArF69GpVKRadOnbC3t1e+GjduTNmyZZk+ffqrTiOEECKbksRMCCFEpru3YQvxt+8Suf1P9rt4sM+5mvL1Jk9Dj7PPuRpxt++S8PgJt1euI+HBQ0K/aKNTz/3N2wEou2QmFiWLc7Tp1xxyb0hydCwui2fq1Jn0IpqIuUsoOnKAsq3ET6O4MXcJh+u1wv7b9li5vN8Tqho1avDo0SMaNmyIqampsr1IkSLkzZsXNzc3cuXKBaRMkd+pUyelW+PLevbsyapVq4iMjHyveIQQQnxaVFptOn09xFsJCwsDICC3CZcTYvUcjRBCiMxQwtiMRflL6jsMvYuJieH8+fOULl0ac3NzfYeT7Uj7Zi1p36wl7Zu+1NygbNmyb3WcPDETQgghhBBCCD2TxEwIIYQQQggh9EwSMyGEEEIIIYTQM0nMhBBCCCGEEELPZB2zTORgZKLvEIQQQmQS+Z0uhBDiQ5LELBN553bQdwhCCCEyUbJWi1ql0ncYQgghPgPSlTGTJCQkEBsrU+VnhdjYWM6dOyftm0WkfbOWtG/Wy8o2lqRMCCHEhyKJWSaSJeGyhlarJTY2Vto3i0j7Zi1p36wnbSyEECI7kMRMCCGEEEIIIfRMEjMhhBBCCCGE0DNJzIQQQgghhBBCzyQxE0IIIYQQQgg9k8RMCCGEEEIIIfRMEjMhhBBCCCGE0DNJzIQQQgghhBBCzyQxE0IIIYQQQgg9k8RMCCGEEEIIIfRMEjMhhBBCCCGE0DNJzIQQQgghhBBCzyQxE0IIIYQQQgg9k8RMCCGEEEIIIfTMUN8BCCGEEEJ8TJKTk0lMTPwg54qPj1f+NTCQz8szm7Rv1voc29fIyAi1Wp0ldUtilolUKpW+QxBCCCHEO9Jqtdy7d4+nT59+sHNqNBoMDQ25c+fOZ/PG9kOS9s1an2v75syZEzs7u0x/7y+JWSYxNjbGzMxM32F8UBqtBgPV5/OfUAghRPaWmpTlzZsXc3PzD/KBa3JyMvHx8ZiYmGTZp/CfM2nfrPW5ta9WqyUmJobIyEgA8ufPn6n1S2KWiXZF7+Jx8mN9h/FB2KptaWzRWN9hCCGEEJkiOTlZScpy5cr1Qc8LYGpq+lm8sf3QpH2z1ufYvqkPYiIjI8mbN2+mXrckZpnocfJjHiQ/0HcYQgghhHhLqWPKzM3N9RyJEOJjl/p7IjExMVMTM+mHJoQQQgjxPzJeXAjxJln1e0ISs4/EpX2XmN5gOiMKj2BMqTFsHLGRhNiE1x5z99xdhhUcxuUDl9Pdf2jlIQbaDtTZFrIwBO/i3owpNYaDyw4q25MTk/Gv48/9y/ffKX5HR0dMTU2xtLTEysoKCwsLChQowLBhw9BoNAAsW7aMChUqYGVlhbW1NTVr1mT79u069Wi1WhYtWkTlypWxtLTExsaG+vXr8/vvv79TXEIIIcTH7nOaNEEI8Wrym+Aj8OLhCxa3X4xHNw/8rvsx9O+hXDlwhT0z97zymISYBFb2WElibPrT+d49f5fNozfrbIt7HseW0Vvos7UPvTf2ZuPwjcRHp0xzGjI/hNINSpOvRL53vo4FCxbw4sULnj9/TnR0NH/88QcrVqzAx8eH1atXM2rUKObNm8ezZ8948OABPXr0oHXr1uzbt0+po1OnTvj5+eHt7c39+/eJjIxk8ODBzJ8/n969e79zbEIIIcS70mg1WVa3Wq3GzMws3e5Q73LePn368NVXX6XZ3qFDB5ycnAgNDdXZvmvXLpycnLh3795bnysjtFotmzZt4tGjR68s4+TkpPPl7OyMu7s7P/zwA5cuXXrvGJYvX46Hhweurq7s3r37vevLqJEjR/LNN99k6Tnq1auHk5MTy5YtS3f/2LFjcXJyIiAgIMN1PnnyhA0bNmS4/NGjR3F2dubWrVsZPiazBAQEUK9evQyX/+uvv7hy5QoAhw8fxsnJSS9xv4qMMfsIWOa25OeLP2NqZZoy28vjGBLjE7HMbfnKYzYM20BZz7LcPX83zb6EmARWdl9JrR9qETwtWNluoP5fHq5N+UWJKuVR7NPbTzmy7giDdg/K1OsqW7YstWrV4vjx4zx48ABXV1eqVq0KpMxi2aVLFyIiInjy5AkAW7ZsYePGjZw/f56iRYsq9Xh6egIpf1Tat29PkyZNMjVOIYQQ4nUMVAYffIKvd51kq3r16vj5+REXF4epqSkAz58/5/Tp0+TPn599+/ZRpUoVpfzRo0cpWrQodnZ2mRb7y44cOcLIkSPZs+fVHzYD/PjjjzRt2hRImYI9MjKSCRMm0K1bN/788893HvsXFRVFQEAA3bt3p0OHDtja2r5TPe/C29tbmRwjKxkZGbFr1y6+++47ne1JSUn8+eefb93tbsqUKdy6dYt27dplZphZolu3bnTq1ClDZW/fvk2vXr1YuXIlxYsXx83NjQMHDnzQe+JNJDH7SJhapfzyHO8ynmd3n1G0WlGqdKySbtnQtaE8vPqQDrM78Kf/n2n2/zbsN5wbOVOydkmdxMzY3Jg2U9uw6OtFqAxUtJ/VHmNzY371+hXPMZ4Ymxln2vUkJiZy8OBB9u7di4+PD2XKlKFRo0Y0btyYZs2aUbVqVVxdXRkzZoxyzKZNm/Dw8NBJylIVLVqUqlWrsmHDBknMhBBCfHCfygRf1apVIzExkbCwMCpXrgzAP//8Q44cOWjXrh1//vknQ4cOVcofOXIEDw+PLItHq9VmqJyVlRV58uRRXufLl48RI0bQoUMHDh06RP369d/p/M+fP0er1VK9enUKFiz4TnW8Kysrqw9ynmrVqrF//37u3r2rM337v//+i7m5+Vsv55TRn9nHwMLCAgsLiwyV/e91GRsb69xzHwPpyviR8T7qjc9ZHwzUBizrmvax9P1L99kxYQddFnf5/ydgLzm6/ij3L92n6Y9N063fo6sH48+MZ9zpcVTpUIULey+QnJRM0apFWdZ1GVNqTeG3Yb+RnPj2n/B4eXmRM2dOcubMSZ48eejbty9Dhgyhb9++1KtXj2PHjuHo6MjMmTOpXLkytra29O7dmxcvXgBw586d135ilz9/fu7cufPWcQkhhBCfi9SnX8ePH1e27d+/n+rVq1OzZk0uXLigrMEUFRXFpUuXqFGjBgBxcXHMnDmT+vXrU7ZsWVq1aqXT9S8oKIh69erh6+tLpUqV6NWrFwCBgYE0aNAAFxcX6tWrx9y5c9FqtRw+fJguXboAUL9+fYKCgt7qWgwNU54fGBunfHD8/PlzxowZQ9WqValYsSJdunQhLCxMKR8QEED79u0ZPHgwFSpUwNPTk4YNGwLw3XffKV3enj59io+PD7Vr16ZcuXJ06NCBo0ePvrIeHx8fgoKCaNiwITt27KBevXqUK1eO77//nvv37+Pr60vlypWpXr06CxcuVOp5uStjare5kJAQmjVrhouLC56envz1119K+eTkZGbMmEGNGjVwdXWlX79++Pr6vrE7ZLly5ShQoAC7du3S2b5jxw6aNGmS5onZ8ePH6dSpE+XKlaNOnTr4+Pgo78VGjhzJpk2bCA0NxcnJCUi5T8aNG0ft2rUpU6YMHh4ejBs3jri4OJ16Q0JCaN68uXJtf//9t7JPq9WyePFi6tevj6urKy1btmTr1q3K/vS6Fd66dQsnJycOHz6sxNa3b1+6detGhQoVWLhwYZqujJs3b8bT05OyZctSs2ZNfH19SUhI4NatW0py36VLFwICAtKcMykpSanP1dU1zXCbD0ESs4+MsZkx1vmtaT6uORf2XCDmaYyyLzEukRXfr+DLiV9iY2+T5tj7l++zzWcbXZZ0QW345qk7k+KT2Dp+K639WhM8PRhzG3OGhQzj4bWH/PvLv28d+7x583j69KnydebMGby9vZVfCOXKlWPBggVcuXKFyMhIFi1axO+//66MHcufPz8RERGvrP/69euZvpCfEEIIkd1Uq1aNEydOKK8PHDhAzZo1cXFxIWfOnOzfvx+AY8eOoVarla6NgwcPZvPmzXh7e7N161YaNGhA3759dboh3r59m/v377Np0yaGDBnC3r17WbBgAT4+PsrTuPnz57N161bc3NyUsU0bNmxQuiq+iVarJSIigqlTp5IvXz7c3NzQarX06NGD69evs3DhQtavX0/58uXp0KED586dU449ceIEuXLlYsuWLQQEBLBu3ToAZs2axW+//UZycjLdunXj6NGjTJ48mU2bNlGqVCm6du2qk+S9XM+3334LwN27d1mzZg3z5s1j2bJlhIWF0aJFCwwNDVm/fj3t27dn+vTprx0XN3XqVLy9vQkKCqJQoUIMHTqU6OhoAPz9/Vm3bh1jx44lKCiIvHnzsmrVqgy1WZMmTXQSs4SEBHbv3q0MB0l14cIFunbtioeHB1u3bsXf35+zZ8/SrVs3tFot3t7eNGnSROnmBzBixAhOnz7N7Nmz+eOPPxg1ahRBQUFK26ZauXIlo0ePZtu2bTg6OjJw4EDl2mbMmMHq1auV/V26dGH8+PH8+uuvGbq+VMHBwVSvXp2NGzfSokWLNNc2evRo+vXrxx9//MHEiRPZsmULS5YsIX/+/Mq4uYCAALp165am7okTJ/Lrr78ydOhQtm3bRu3atfHy8lLGpH0I0pXxI3Dt8DXW9F/D8P3DMTRO+ZEkJSShNlZjbP7/3QtvnLjBg/AHrOm/hjX91yjbF3dYTOWvK2Od35rYZ7FMrT0VAE1yyqDhkY4jaeffjoptK+qcd2/AXty+dMO2kC13z9+lnGc5VCoV9q723D2Xduza+yhcuDAjRoygT58+AOTJk4cOHTrw8OFD5dOldu3a0bp1a8LCwihbtqzO8RcuXODkyZOMGzcuU+MSQgghsptq1aoxceJEtFot4eHh3Lt3Dw8PDwwMDJRub23atOHIkSO4ublhbm5OeHg4e/bsYcGCBdStWxeAvn37cvHiRRYsWKDTldDLy4tChQoBcPDgQUxMTLC3t6dAgQIUKFCAvHnzUqBAAYyNjbG2tgbA1tZWGfOWnnHjxvHzzz8DKcMhkpKSKFOmDHPmzMHS0pJDhw5x4sQJDh06pIwJGjx4MMePH2flypVMmjRJqat///5KN8IbN24AYG1tja2tLSEhIZw9e5Zt27ZRsmRJIGWCjFOnThEYGMjMmTPTref48eMkJiYyZswY5bhq1apx8uRJhg8fjkql4ocffmDu3LlcvnxZKfNfAwcOpFq1asr3LVu25NKlS5QqVUqZKO2LL74AYMyYMToJ9us0adKEwMBApTvjwYMHsbGxwdnZWadcYGAg1apVw8vLC0iZVXvatGk0aNCA0NBQ3N3dMTU1xcjISOnm5+HhQaVKlShVqhQA9vb2/PLLL1y8eFGn7h9//BF3d3cgZRKa3bt3Ex4eTvHixVm+fDlTpkxR7q3ChQtz+/ZtAgMDMzxGDFJ+jt27d093361bt1Lex750LwYGBmJpaYlarVbuG2tr6zTdH1+8eMH69esZPXq08gHCgAED0Gg0SnL5IUhi9hEoUKYACTEJbPPZRvNxzYm6H8WWMVuo2rmqkqgBFKtWjKl3puocO9B2ID3W9KBEjRIAfDHkC2Xf5QOXmdtiLpOuT+K/Ht14xOnfTzPwz4EA5CmWh+tHruPeyZ0bx2/g3NA5zTHvo3PnzkyYMAEHBwdq1qyJhYUFZ8+eJTAwkDZt2gDQrFkzOnXqRPPmzZk9e7byR2D79u0MGTKEdu3a0axZs0yNSwghhMhuqlWrxtOnT7l69SoHDhygVKlSypvsGjVqMH36dCBl4o/UbmCpb7IrVtT9ELdSpUpMmzZNZ5ujo6PyfYsWLdi4cSNffPEFTk5OeHh40LBhQwoUKPBWMffv319JSNRqNTY2Njpvns+ePQuQZqxZQkIC8fHxyutcuXK9dmzXpUuXsLKy0kmcVCoVlSpVUp4kvq6eIkWKKN+bmZlhb2+v9AwyMTEB0Innv14eR29pmTLJW2JiIuHh4cTFxVG+fHmd8hUrVuTChQuvrC+Vi4sLhQoVUiYB2bFjR7rvmc6dO0dERARubm5p9oWHhyuJ1cs6duzI3r172bJlCzdu3ODSpUvcvHlT5z4A3bbJkSMHkNI99sqVK8THxzNixAhGjRqllElKSiIhISFNl8jXcXBweOW+mjVr4ubmRps2bXB0dKR69erUr18fFxeXN9Z77do1EhMT07T/oEGZOzHem0hi9hEwsTSh14ZebPpxE2OcxmCaw5RKX1Wi0dBGAAwvNJyvpn9FpXaVMu2cQSODaPFTCyXxazCwASu+X4F3cW9K1i5J9e+qZ9q5AHx9fSlQoADjx4/n4sWLaDQaihQpQvfu3RkwYIBSbtmyZSxduhQ/Pz+lX3qpUqX44YcfGD58eKbGJIQQQmRHefPmpXjx4pw4cYIDBw4oY8ggJTHz9vbmzJkznDt3TmcSrvRoNBplrFeql5982drasmXLFk6cOMHBgwc5cOAAS5cupV+/fvTt2zfDMefKleu1b7o1Gg2WlpbpjlNLHYP239jSo9Vq052l8L/X+ap6jIyMdF6/7Rp0L8f6ckyp536fiTdSuzN27NiRPXv2pDvlvUajoXnz5sr4wJelNzuhVqulV69eXLx4kebNm9OoUSMGDx6c7n2TXltotVrlmmbOnJnuBG8vt8nL15+UlJSm7Ot+viYmJqxcuZJz585x4MABDhw4wNq1a2nVqhV+fn6vPA7S/lz1RRKzj4RdKTt6B6W/TteUm1NeedzMxzNfua9EjRKv3N9jdQ+d19Z21vTf3v+Ncb7K9evXX7tfpVLRt2/fDP2S7tatm07f35iYGM6fP59lq6wLIYQQ2U3qOLNjx47pdP2ys7OjePHirF27FgsLC8qUKQOgPEE6duyY0t0MUp6qFS9e/JXn2bJlCy9evKBTp05UrFiR/v37M3r0aHbs2EHfvn0z7W93yZIlefHiBQkJCZQoUULZPnr0aEqVKkXnzp0zVI+Tk5My6cnLT82OHTv22uvMag4ODpiamnLy5ElKly6tbD99+nS6yVx6mjRpwqJFi/jtt98oVKgQxYoVS1OmRIkSXL58WScJvnr1KlOmTGHw4MFYWVnp/MzOnTtHSEgI69evx9XVFUh5wnfjxg2lO+ubFC1aFENDQ+7cuaNzb61cuZIrV67w008/KYlR6iQkwGvnHUhPSEgIYWFh9O3bF2dnZ3r27Mn8+fNZsGABfn5+r70XHRwcMDIyIiwsTOmyCdC2bVsaN278yu6TmU0m/xBCCCGEyGaqVavGjh07AKhQoYLOvho1arB9+3aqV6+uPOUoXrw4tWvXxsfHh7/++otr164xZ84c9uzZk+5ECani4+OZPHkymzdv5tatWxw9epTQ0FClq1zq+mMXLlx4r7E6NWvWpHTp0gwcOJBDhw4RERHB5MmT2bhxY7oJyKt4eHjg5OTEkCFDOHz4MOHh4fj4+HDp0iVlkg99MDMz45tvvmH27Nns3r2ba9eu4e/vz8mTJzNcR+nSpXFwcGD69OlpJv1I1a1bN86fP8/YsWO5cuUKp06dYujQoVy7dk3pmmhubk5kZCQ3b94kd+7cGBoasnPnTm7evElYWBgDBw7kwYMHJCQkZCguKysr2rdvz8yZM9m8eTM3b95k06ZNTJ06ldy5cwMpibeFhQXz588nIiKCI0eOMGPGjLdK7A0NDZk7dy7Lly9XYv3rr7/S3IuXLl3i+fPnOseamZnRuXNnZs2axZ49e7hx4wYzZszgypUrOslkVpMnZkIIIYQQb2Cr/rCL0L7v+dzd3UlISKBmzZppnrjUqFGD5cuXp1m/bMaMGUyfPp3Ro0cTFRVFiRIlCAgIUKacT89XX33Fs2fPmDdvHnfv3sXa2ppGjRopa6WVLFmS2rVrM3DgQAYPHvzaJO911Go1S5cuZerUqQwaNIjY2FiKFStGQECAMplGRhgaGrJs2TImT55Mv379SEhIoEyZMixfvjzN+KIPbcCAASQmJjJ69GhiY2OpW7cu9evXf+2Ytf9q0qQJ8+fPf+UMmOXLl2fJkiXMmjWL1q1bY2ZmRtWqVRkxYoRyn7Rq1Yrg4GCaNWtGcHAwkyZNIiAggF9//ZU8efJQp04dunbtyp49ezLc9XLUqFHY2toye/ZsIiMjsbOzo2/fvvTs2RNIGW/n7+/PtGnT8PT0pEiRIowaNeqtnlR5eHjg6+vL0qVLmTFjBqamptSuXZuRI0cCYGNjQ5s2bZgyZQoRERFp7uvBgwdjaGjI+PHjiYqKwsnJiUWLFr1V4v++VNpPaRW5j1Tq9KphDmGfxOKTmSGPOg8dc3T8IOdK7cpYunRp5dMOkXmkfbOWtG/WkzbOWp9L+8bFxXHt2jWKFCmSZhyLRqvBQPXhOxnp67zZSXJyMnFxcZiamqJWv3kpIX0KDg6mYsWKOmO9unXrhp2dHRMnTtRjZK/2KbVvZnrd7wv4/9zgv7OMv4n8bxdCCCGEeI2sTI6Sk5OJjY0lOTn5g55XfHwCAwMZMmQI58+f5+bNmyxfvpx///03zXpdIvuSroyZyNHIERuDtAs/Z0fWausPdi6VSoWZmZlM/iGEECJb0mg0+g5BfAT8/f2ZNGkSXbt2JS4ujuLFizNr1iyqVq2q79DEByKJWSaqbpa5U8yLFGZmZmkWSHwbWo0G1VtOZyuEEEII8SHZ29szZ84cfYch9EgSs0yUFPQr2gf39R2GeIkqTz4MW2d8RXkhhBBCCCH0QRKzTKR9cB/u3dZ3GOIlMrONEEIIIYT4FEhiJt7K+jMX+XbjDkwN///WaVm6OMtbN0lTNuDf4wT8e5xHMXE45MzB6DpVae2cspjjk9g4Bu7Yy59XrpOQrKFSwXxM/qI25fPnBWDOv8fxDfkXQwMDxtSpRs/K/1vUMDkZj8VrWNW2KU65P+zUxUIIIYQQQmSVjyoxmzdvHocOHWLVqlXKtlGjRhEUFKRTLl++fOzbtw9IGTA7Z84cNmzYQFRUFBUrVmTcuHE6K5r/15MnT5gwYYJSR+PGjRk1alS2ngY4sxy7fY9Ors4sadXoteV2Xb7G5P2h7PnuK5xy2xJ07hIdN2znQv98ONpY88PWP0lM1nC+fzcsjI0Y/9c/tF27hSuDevA8PoFhf4QQ2qszWi24L/yFTq7OWBgbMfvf4zQq4ShJmRBCCCGEyFY+mhkRli9fzuzZs9Nsv3jxIr169eLAgQPK1+bNm5X98+bNY+3atUyYMIF169ahUqno0aPHa1cj79+/vzIN6ezZszl48CA+Pj5ZcVnZztE796hYIN8by1148BitVotGq0Wr1aJWGWCsNsDwf5Nw/NrWkzXtmpHTzJQXCYk8i4sn9/8SY/X/Zl/UalO6IqpUKlTArWfP+eXUOX6s5Z5VlyeEEEIIIYRe6P2J2f379/H29ubYsWMUKVJEZ19ycjJXrlzBy8uLPHnypDk2ISGBpUuXMmzYMGrXrg2krFpfs2ZNgoOD8fT0THPMiRMnCA0NZceOHcpK3j/99BPdu3dn8ODB5Mv35qTjc6XRaDlxNxILIyOmHTxCskZL4xJFmNiwJjZmuovrfV3WiRUnz+A6dwVqlQqVSsXy1k2wt7YCwEitxkgNY/YcYMr+UKxMjNnS8UsAzI2NmO1Zn1arN2GgUrGgRUPMjY3otnkXP9evgZmR0Qe/diGEEEIIIbKS3p+YnT17Fmtra7Zu3Yqrq6vOvuvXrxMfH68kUP914cIFoqOjddZ3yJEjB87Ozhw5ciTdY44ePUqePHl06qxSpQoqlYpjx45lwhVlXw9iYihvl5fWziU53acrId+358rjJ3QN2pmmbEKyBle7vPzToyPPvPszv3kDftjyJ2H3H+iU+7GWO1Gj+zO6djWa/RLE1cdPAehRqRxXB/fkyqAedClfhuAr10lM1uBRuCDt12+j0vxV9N++h8R0FuQUQgghPiUGsqSLEIKP4IlZvXr1qFevXrr7Ll26hEqlYsWKFezbtw8DAwNq167NwIEDsbKy4t69ewDkz59f57i8efNy9+7ddOu8f/9+mvLGxsbkzJnzlceIFPksLdjb7WvldWFjI/wa1sJj8WqexydgZWKs7BuwYy/VCxWgUkE7AL51c2Ft2AVWnTzHlEa1lXKpT78GVq/I0uNhbLsYzoBqFXXOG5+UxKjg/Wzs0JLJ+w9ja2bKkV6daf5LEMuOn1EmBnmd2NhYtFqZozE9sbGxOv+KzCXtm/WkjbPW59K+8fHxaDQakpOTSf7Ph34GKlWWrYepVqsxMzNLd59Wo0HzHn+7Xrx4Qa1atbCwsGDPnj0YGxu/+aBPUIMGDbhz5w7Dhw+na9euOvu0Wi2+vr5s3LgRLy8v+vbtm6E6nz59yp49e2jTpk2GyoeGhtK1a1eCg4MpWLDg217Ce5kzZw6bN29m9+7dGSr/999/Y29vT/Hixd877tT3VlqtNs3/m+wsOTkZjUZDbGxsugvEa7VaVP8bmvM29J6Yvc7ly5cxMDCgYMGCLFiwgIiICCZPnsylS5dYsWKF8kfiv79oTExMePbsWbp1xsbGpvuLycTEhPj4+My/iGzk9L0HrA27gG+DGsrNFp+UjIFKhbFa9w/WzWdRxP9nLJqRgQFG/ytXa8kaBlSrSJsyJZX9CcnJabpEAkw7eJR2Lk445MzB2chHtCxVHJVKRfn8+TgT+TBDsV+7di3bv6l4X9evX9d3CNmatG/WkzbOWp9D+xoaGqZ5L2BgYICZmdkHX6s0dR3OhFe88cuILVu2YGNjw+PHj9m5cyeNGr1+4q5PlVarxdDQkJ07d9K+fXudfUlJSezZsweVSkVSUhJxcXEZqtPPz487d+6kOywmPalzG8THx2f4HJmlY8eOtGnTJkPnvXPnDl5eXixatAh7e3tKly7Nn3/+Sc6cOd8r7s/tPXR8fDxJSUlcvXr1lWXe5YOQjzox69evH127diVHjhwAlCxZkjx58vD1118TFhaGqWnKm/iEhATle0hprFd9+mRqapruxCDx8fEyK+Mb2JqZMj/0JLZmpgysVpE7z18wMngfXcqXwcRQ91Zq5lSM+aEnaVayKK52edl0/jJ/X7/JT/VrAFDFPj8//X2ISgXtsLM0Z9L+UOKTkmnupNtt9fqTZ2w+f4UD3TsAUNw2J//eusu3bmU4evseTUrqjkt8lSJFisgTs1eIjY3l+vXrODo6vvL/jXh30r5ZT9o4a30u7RsfH8+dO3cwMTHReU+R6kOvVZr6F+t9nnJt27aNmjVrcv/+fYKCgmjZsmXmBPeRUalUVKtWjQMHDvDkyROdnlEHDhzAzMwMMzMzDA0N0/3ZpketVmNgYJDh8qk/p1fdP1npbc5nYmICpMRramqKqakpVlZW73xurVZLfHw8JiYm7/SE6FNmaGhI4cKFlTZ92ZUrV96tzvcNKiupVColKUtVsmTKE5Z79+4p//EiIyMpXLiwUiYyMpJSpUqlW6ednV2aR70JCQk8ffpUJv54A3trK7Z0asXo3Qfw23cYU0M1X7k44dewFgA2vgHMbd6AjuVKM6Z2NdQqFV+v38bj2DiK29rwW/uWyjplvg1qoFapqLVkDQnJybjb5+ePb9uleWI2aOdfTP6iFsaGagCG16xCpw3byT9lPvWKFqZnpXIZij07v5nILGZmZvLhRBaS9s160sZZK7u3r4GBAQYGBqjVatRqtb7DUbxrLOHh4Zw+fZru3bsTExPDyJEjuX79OsWKFWPkyJGEh4ezYcMGpfy9e/eoW7cuS5cupVq1ahw/fpxp06YRFhaGra0tdevWZciQIVhaWgIpQ1EaNGjAgQMHePToEbNmzcLZ2Zlp06bx999/8/DhQ3LmzEmDBg0YNWqUkjycOXMGPz8/zpw5Q+7cuRkwYAAjR45k2bJluLu7o9VqWbJkCWvXruXhw4c4Ojry/fff06JFi1deq0qlwtXVlatXrxIcHMx3332n7Nu1axdffPEFwcHBys8XeO31jRw5UpkB3NnZmYsXLxIVFfXaa0sdJ7h//37Wrl3LtWvXcHBwYNiwYdSpUwfgjdd2+PBhunTpwp49e7C3twfg1q1b1K9fn5UrV+Lu7s7IkSN58eIFMTExnDx5kh9++IGEhAQ2bdrE3r17Adi8eTOLFy/mxo0b5MyZk8aNGzNs2DAiIyNp2LAhAF27dqVv375UqVJF55xJSUnMnz+fTZs28ejRI4oVK8bAgQOpVatWum2f2n1RpVJ9VP9vslpq4m5mZpZuYvyuSepHnZgNGTKEp0+fEhgYqGwLCwsDoHjx4hQqVAhLS0sOHz6sJGZRUVGcO3eOzp07p1tn5cqV8ff3JyIiQlnr7PDhwwBUqFAhKy8nW6jlWIh9/3t69V9PvPsp3xuqDRhbtzpj61ZPt6yJoSGTG9Vm8kvjzdKzqWMrndf5rSx1xrkJIYQQIq3ffvsNc3NzatWqRVJSEsbGxqxZs4bRo0fz5Zdf0qVLF533Qlu3biVfvny4u7tz4cIFunbtSq9evfD19eXhw4dMmTKFbt26KUsTAaxZs4aFCxdiZWWFk5MTAwYM4N69e8yePZtcuXJx8uRJRo0aRdGiRfn222+5f/8+3377LfXr18fHx4fbt28zfvx4nbFJM2bMYNu2bYwdO5ZixYpx5MgRxo8fz/Pnz+nUqdNrr7lJkybs2rVLScwSEhLYvXs3CxYsIDg4WCn3puvz9vYmLi6Oe/fuERAQAMCIESNee22pVq5cyU8//UTevHnx9/dn4MCBHDx4EAsLi/e6tpcFBwczbNgwxowZg6mpKb/99pvOtY0ePRp/f3/KlStHeHg4Q4YMwcbGhh9++IENGzbQrl07AgIC8PDw4MyZMzp1T5w4kR07djB27FhcXFzYtGkTXl5ebN68meLFi2c4RvFuPurErFmzZvTu3Zv58+fj6enJtWvX+Omnn2jWrJkyq2Lnzp3x9/fH1taWggULMnXqVOzs7JRPBJKTk3n8+DFWVlaYmpri6upKhQoVGDRoEOPHjycmJoZx48bRqlUreWImhBBCiE9eUlIS27Zto27dukqPkdq1a7NlyxaGDBlClSpVKFSoENu2bVMmw9i2bRstW7bEwMCAwMBAqlWrhpeXFwCOjo5MmzaNBg0aEBoairu7u1Jn9er//wGsh4cHlSpVUnot2dvb88svv3Dx4kUA1q1bR44cOfD19cXIyIjixYszZswYevfuDUBMTAzLly9nypQp1K1bF4DChQtz+/ZtAgMDM5SYBQYGcvfuXfLnz8/BgwextbVN04sqI9dnamqKkZGRslzTm64t1Y8//qi0T58+fdi9ezfh4eEUL178va7tZdbW1nTv3j3dfbdu3UKlUmFvb0+BAgUoUKAAgYGBWFpaolarsbW1VeqwsLDQOfbFixesX7+e0aNH07RpUwAGDBiARqMhOjo6w/GJd/dRJ2Z169Zl1qxZLFiwgAULFmBlZUXz5s0ZOHCgUqZ///4kJSUxevRo4uLiqFy5MoGBgUpf37t371K/fn38/Pxo3bo1KpWKOXPm4OPjw7fffouJiQmNGzdm1KhRerpKIYQQQojMExISwoMHD5Q31wBNmzYlODiY7du307ZtW1q1aqUkZufPn+fSpUvMnj0bgHPnzhEREYGbm1uausPDw5XEI/VpW6qOHTuyd+9etmzZwo0bN7h06RI3b97E0dFRqbdMmTIYvbQeaaVKlZTvr1y5Qnx8PCNGjNB5X5aUlERCQgJxcXGvHU/l4uJCoUKFlKdmO3bs0GmDVBm9vre5tlQvr8mbOhwnLi4uQ9eWUf9t95fVrFkTNzc32rRpg6OjI9WrV6d+/fq4uLi8sd5r166RmJhI+fLldbYPGjQow7GJ9/NRJWaTJk1Ks61Ro0avnUVIrVYzbNgwhg0blu5+e3v7NJ9m5MqVS/nlI4QQQgiRnQQFBQEpH17/19q1a2nbti1ffvklc+bM4fTp0+zcuRM3NzclqdBoNDRv3pxevXqlOT71iQvoTjqh1Wrp1asXFy9epHnz5jRq1IjBgwczZswYpYxarX7tDJOpk3TNnDmTokWLptmfkYlQUrszduzYkT179rBu3bo0ZTJ6fW9zbanSW5NOq9W+1bW9PFlZUlJSmrKvS05NTExYuXIl586d48CBAxw4cIC1a9fSqlUr/Pz8XnkcoJMwC/2QFQ2FEEIIIbKJx48fExISQuvWrdm8ebPOV9u2bQkLC+Ps2bMULFiQKlWqsGvXLnbs2MGXX36p1FGiRAkuX76Mg4OD8pWcnIyfn98r13w9d+4cISEhzJ49m6FDh9KiRQsKFy7MjRs3lESjVKlSnD17lsTEROW4U6dOKd8XLVoUQ0ND7ty5o3PukJAQAgMDM7QQd5MmTTh16hS//fYbhQoVSjcJysj1vTx5Q0au7U0ycm2pidGLFy+U4yIiIjJUf6qQkBDmzJmDs7MzPXv2ZOXKlfTv358dO3akua7/cnBwwMjISJnPIVXbtm1ZsmTJW8Uh3s1H9cTsU6cqUQpt7rz6DkO8RGWT9pMvIYQQIrvasmULSUlJdO/eXRmPn6pXr15s2rSJNWvWMGHCBFq3bs1PP/1EUlKSTpe/bt260alTJ8aOHUuXLl2Ijo7Gx8eH6OjoNF33UuXOnVtZS8zW1panT5+yYMECHjx4oCxT1LFjR5YtW8aYMWPo0aMHkZGR/PTTT0BKwmBlZUX79u2ZOXMmFhYWVKxYkaNHjzJ16lR69OiRoesvXbo0Dg4OTJ8+nR9++CHdMhm5PnNzcyIjI7l582aGru1NMnJtJUuWxMLCgvnz5zNkyBAiIyOZMWPGW83wZ2hoyNy5c7G0tKR+/fo8ffqUv/76S+m2mTqz6qVLl3B2dtY51szMjM6dOzNr1ixsbW0pUaIEGzdu5MqVK8q4OJG1JDHLRIb10vZjFp+vZK0W9We2pocQQmRXqjz5+JCrYaryvNuEZEFBQVSvXj1NUgZQqFAhGjZsyPbt2xk5ciSNGjXip59+okGDBjprWZUvX54lS5Ywa9YsWrdujZmZGVWrVmXEiBGv7E6YL18+Jk2aREBAAL/++it58uShTp06dO3alT179qDVasmVKxdLlixh4sSJtGzZEjs7Ozp06MCUKVOUp0WjRo3C1taW2bNnExkZiZ2dHX379qVnz54ZboMmTZowf/78dMeXZfT6WrVqRXBwMM2aNSM4OPiN15YRb7o2S0tL/P39mTZtGp6enhQpUoRRo0a9cqKP9Hh4eODr68vSpUuZMWMGpqam1K5dm5EjRwJgY2NDmzZtmDJlChEREcpkeakGDx6MoaEh48ePJyoqCicnJxYtWpTu/SQyn0orq+6+t9RHvlvz5yAi8fNa+Vykz8HIBO/crx6c+zGJiYnh/PnzlC5dOluvUaQv0r5ZT9o4a30u7RsXF8e1a9coUqRImjE8Wo0GVQa60WU2fZ03q1y5coVnz55RsWJFZdvx48fp0KEDf//9t87C0JklOTlZmTTkc1pn60P5XNv3db8v4P9zg7Jly75VvfLELBNFJMZzOSFW32EIIYQQIhNlZXKUnJxMQkICxsbGad7YZqekDOD+/fv07NkTX19fKleuTGRkJH5+flSpUiVLkjIhPjXZ63+8yLbub97OPudqOl8hJSoRUrJyuuVPf9uHkJJVdMo/+vsgQJp69pWqyt+O5bm/ZScAt5at5oBbHQ5Wqs/tXzYodWoSEznq2Z6Y8OtvHb+joyOmpqZYWlrqfKlUKkxMTIiJiVHKxsXFYWlpScGCBXW6R1y9ehWVSqWzGOTcuXNRqVTMmDHjrWMSQgjxcXjdTIXZiYeHB97e3ixcuJDGjRvTp08fnJyclEWchfjcyRMz8UnI18qTfK08ldfx9+5zrEVnio4amG7552HncF05j5xVK6XZV+vcIZ3X5wePJuHhY/J4NiTpRTRXJkyj0va1oNVytFkH7Fo3Q21uxq2lv2JbxwPzYo7vdA0LFiyga9euOtuio6PJlSsXBw4c4IsvvgBg9+7dODo6cvPmTQ4fPkzVqlUBCA4OxtHRUWctkrlz59K7d29mzZpFv379MDSU/9JCCCE+Xh07dqRjx476DkOIj5I8MROfHK1Wy/lBo8lVrwZ2X3qm2R978zaJT59h6VL6jXXd3bCFJ/v/xXnWRAwMDVGpDVJPAlptykxIKoi7e5/7Qb/j0C9js0JllIWFBXXq1GHv3r3Kts2bN9OsWTMaN27M5s2ble27d++mefPmyus9e/YQGRnJtGnT0Gg0/Pbbb5kamxBCCCGE+HAkMROfnPubthN9OZxio4emu//5qTOoLcw513c4ByvUJfSLNtxdvzlNuaSo54T7Tqf42GEY2eQEQG1mRsmfRhHWrR9hPQbiNGksajMzwn+eSpFh/VC/ZlHHd+Xp6akkZhqNhm3bttGyZUtatGihJGYajYa9e/fSokUL5biAgAB69OiBmZkZXl5eTJs2LdNjE0IIIYQQH4YkZuKTotVoiAhYhEOf7hhaWqRbRpOQiHUFV4oM7Ue1w39SfPQQrvhMIXL7nzrlbi1fg6l9AfI0+0Jne4FObal26A+qHdyJXdsWPN73D5rEJKwru3HWayhHmnzFpTET0by0QGZGeHl5kTNnTp2v6OhoPD09OX78OM+ePeOff/5BpVLh7u5O06ZNCQ8P5+LFixw/fpykpCRq164NpCw4uWvXLvr06QNAz549OXv2LCEhIW8VkxBCCCGE+DhIYiY+KU8PHSEh8iH5v/7ylWXsWjej3Iq5WLmUwsDICNta1cnXuhmRv/9/YqbVarm7bhP2XTu8duFGTXwC4X4zKTFuODfmLsHQ2ppKO9YRG3GLu+s2v1Xs8+bN4+nTpzpfFhYWFC1alBIlShASEsLmzZtp3rw5BgYG2NjYULNmTXbu3ElwcDCNGjVS1nmZN28eiYmJlC9fnty5c1OyZEkSExPx9/d/q5iEEEIIIcTHQWYKEJ+UBzt3k7tRPdTmZq8sc3f9ZtQW5uT1/P8nYZqERNSmJsrr56fOkPi/CT9e58aiFeRt1ghT+wJEXwwnd6O6qFQqrFxKEX3x8vtf0P94enqyf/9+tm/fztSpU5XtLVq0YM+ePcTExPDdd98BKbM2BgYGEhgYSIMGDZSyZ86coWnTply4cIFSpUplWmxCCCGEECLryRMz8Ul5dvQk1lUqvLZMUtRzLo+bxPMzF9BqNDzau4/ILTvJ36GNTj2WZUujNnt1ghd78zYPd+2hUI8uAJgVKUzU8dNoEhOJOnUWM4dCmXNRpCRmQUFB3Lx5UyfZatmyJaGhoYSGhtKkSRMAVq9ejUqlolOnTtjb2ytfjRs3pmzZskyfPj3T4hJCCCGEEB+GJGbikxJ74xYm+fKm2b7PuRr3N28HwP77zhT8tgNnfhjEfufqhE+aRanpP5PzpYTuVfW87IrPFIp5D8bAOKX7YGGvbsTeuMXBCnUxtLKkQOd2mXZdNWrU4NGjRzRs2FBnBfkiRYqQN29e3NzcyJUrF5AyRX6nTp2Ubo0v69mzJ6tWrSIyMjLTYhNCCJG1DLLZQtJCiHej0r68gq14J2FhYQAE5DbhckKsnqMRH4MSxmYsyl9S32FkSExMDOfPn6d06dKYm5vrO5xsR9o360kbZ63PpX3j4uK4du0aRYoU0fmADCBZq0X9mvHIWeVdz1uvXj1u376tvDYyMqJgwYK0a9eO7t27Z1p8Wq2WzZs3U6tWLeXDw/9ycnLSea1Wq7GysqJ8+fIMGTKEkiXf72/lokWLWLp0KbGxsUybNk2n1wlAcnIycXFxmJqaolar3+tcLxs5ciS3b99m1apVmVbnf6X+HEeOHKkMZ3jZ2LFjWbduHX379qVfv34ZqvPJkyfs3r2bdu0y9uHy4cOH6dKlC3v27MHe3j7N/qxqX0iZfXrTpk06Swq9zl9//UWhQoUoXrz4G+N+X6/7fQH/nxuULVv2reqVMWZCCCGEEK+hVqnwfRhBRGL8Bzung5EJ3rkd3vn4bt260a1bNyDlTeSpU6cYPXo0ZmZmdOrUKVNiPHLkCCNHjmTPnj2vLffjjz/StGlTIGX5l8jISCZMmEC3bt34888/3znhj4qKYvr06fTs2ZOvv/4aW1vbd6rnXXh7e5OcnJzl5zEyMmLXrl1pErOkpCT+/PPP105glp4pU6Zw69atDCdm+tStW7cM36u3b9+mV69erFy5kuLFi+Pm5saBAwc+6D2RGSQxE0IIIYR4g4jE+E+qV4y5uTl58uRRXhcqVIjDhw+zcePGTEvMMtrpysrKSieWfPnyMWLECDp06MChQ4eoX7/+O53/+fPnaLVaqlevTsGCBd+pjndlZWX1Qc5TrVo19u/fz927d8mfP7+y/d9//8Xc3Byz14yVT8+n1FHOwsICC4v0l0b6r/9el7Gxsc4996mQTs1CCCGEEJ+B/76J12q1LF68mPr16+Pq6krLli3ZunWrTpnUGYBdXFyoV68ec+fORavVKl3FAOrXr09QUNBbxWJomPJswNjYGEhJssaMGUPVqlWpWLEiXbp0UbqDQUq3tvbt2zN48GAqVKiAp6cn9erVA+Dbb79Vvn/69Ck+Pj7Url2bcuXK0blzZ44fP/7Kenx8fAgKCqJhw4bs2LGDevXqUa5cOb7//nvu37+Pr68vlStXpnr16ixcuFCpZ+TIkXzzzTdASnc/JycnQkJCaNasGS4uLnh6evLXX38p5ZOTk5kxYwY1atTA1dWVfv364evrq9TxKuXKlaNAgQLs2rVLZ/uOHTto0qRJmidmx48fp1OnTpQrV446derg4+PDixcvlJg3bdpEaGio0sU0KiqKcePGUbt2bcqUKYOHhwfjxo0jLi5Op96QkBCaN2+uXNvff/+t7NNqtQQGBr7yPkptn1u3binbbt26hZOTE4cPH1Zi69u3L926daNChQosXLiQgIAA5ecKsHnzZjw9PSlbtiw1a9bE19eXhIQEbt26pST3Xbp0ISAgIM05k5KSlPpcXV1p3bo1+/bte23b64MkZkIIIYQQ2dzp06fZtm0bX3/9tbJtxowZrF69mtGjR7Nt2za6dOnC+PHj+fXXXwHYu3cvCxYswMfHhz///JOhQ4cyf/58tm7dipubGwEBAQBs2LBB6ar4JlqtloiICKZOnUq+fPlwc3NDq9XSo0cPrl+/zsKFC1m/fj3ly5enQ4cOnDt3Tjn2xIkT5MqViy1bthAQEMCGDRuAlGTrt99+Izk5mW7dunH06FEmT57Mpk2bcHJyonfv3pw5cybder799lsA7t69y5o1a5g3bx7Lli0jLCyMFi1aYGhoyPr162nfvj3Tp0/n0qVLr7y2qVOn4u3tTVBQEIUKFWLo0KFER0cD4O/vz7p16xg7dixBQUHkzZs3w+PTmjRpopOYJSQksHv3bjw9PXXKXbhwga5du+Lh4cHWrVvx9/fn7NmzdOvWDa1Wi7e3N02aNFG6+QGMGDGC06dPM3v2bP744w9GjRpFUFAQ69at06l75cqVyn3i6OjIwIEDlWubM2fOa++jjAoODqZ69eps3LiRFi1apLm20aNH069fP/744w8mTpzIli1bWLJkCfnz59e5F1K78L5s4sSJ/PrrrwwdOpRt27ZRu3ZtvLy8uHLlylvFmNWkK2MmcjAyeXMh8VmQe0EIIYQ+LVy4kKVLlwKQmJhIYmIirq6uSgIVExPD8uXLmTJlCnXr1gWgcOHC3L59m8DAQDp16sSNGzcwMTHB3t6eAgUKUKBAAfLmzUuBAgUwNjbG2toaAFtb23QnQEg1btw4fv75ZyWWpKQkypQpw5w5c7C0tOTQoUOcOHGCQ4cOKWOCBg8ezPHjx1m5ciWTJk1S6urfv7/SjTD1aYi1tTW2traEhIRw9uxZtm3bpkwqMnr0aE6ePMnSpUuZNWtWuvUcP36cxMRExowZoxxXrVo1Tp48yfDhw1GpVPzwww/MnTuXy5cvv3LCkoEDB1KtWjXl+5YtW3Lp0iVKlSrF6tWrGTVqFF98kbLG6pgxYzhx4kQGfpIpiVlgYKDSnfHgwYPY2Njg7OysUy4wMJBq1arh5eUFgKOjozIhSmhoKO7u7piammJkZKR08/Pw8KBSpUrK+qf29vb88ssvXLx4UafuH3/8EXd3dwD69OnD7t27CQ8Pp0iRIqxevZpJkya98j7KKGtr61dOTnPr1i1UKpXOvRgYGIilpSVqtVq5b6ytrdN0f3zx4gXr169n9OjRyv0/YMAANBqNklx+LCQxy0TvM0hXZD/6msVLCCGEaN++vdJNLikpievXrzNjxgw6duzIxo0buXLlCvHx8YwYMYJRo0YpxyUlJZGQkEBcXBwtWrRg48aNfPHFFzg5OeHh4UHDhg0pUKDAW8XSv39/JSFRq9XY2NjovHk+e/YsQJqxZgkJCcTH//+EK7ly5Xrt2K5Lly5hZWWlkzipVCrc3Nz4999/31hPkSJFlO/NzMywt7dXugqamKR84PpyPP9VtGhR5XtLS0sgJRENDw8nLi6O8uXL65SvWLEiFy5ceGV9qVxcXChUqJAyCciOHTto1qxZmnLnzp0jIiICNze3NPvCw8OVxOplHTt2ZO/evWzZsoUbN25w6dIlbt68iaOjo065l9smR44cQMqkMuHh4cTHxzNq1Ci8vb2VMi/fRxnl4PDq99E1a9bEzc2NNm3a4OjoSPXq1alfvz4uLi5vrPfatWskJiamaf9BgwZlOLYPRRKzTJKQkEBsbOxbD8IUbxYbG6tMSfopta8kZUIIIfTF2tpa541usWLFsLa2plOnTvzzzz/Y2NgAMHPmTJ2EIpWxsTGmpqZs2bKFEydOcPDgQQ4cOMDSpUvp168fffv2zXAsuXLleu2bbo1Gg6WlZbrj1FLHoAGvfSoHKd0k05ulUKPRKGPaXlfPf9cHfdv15V6O9eWYUs/9PhNvpHZn7NixI3v27FG67r1Mo9HQvHlzevXqlWZferMTarVaevXqxcWLF2nevDmNGjVi8ODBjBkzJk3Z9NpCq9Wi0WgAmD59OsWLF09T5uU2efn6k5KS0pR93c/XxMSElStXcu7cOQ4cOMCBAwdYu3YtrVq1ws/P75XHQdqf68dMxphlok9ppptPiVarJTY2VtpXCCGEyAQajYaiRYtiaGjInTt3cHBwUL5CQkIIDAzEwMCALVu2sGbNGipWrEj//v1Zv3497dq1Y8eOHQBvPVX7q5QsWZIXL16QkJCgE8vixYvfOBX/y5ycnIiKikozDuzkyZMUK1YsU2J9Fw4ODpiamnLy5Emd7adPn85wHU2aNOHUqVP89ttvFCpUKN3rKVGiBJcvX9Zpw+TkZPz8/Lh79y6g+zM7d+4cISEhzJ49m6FDh9KiRQsKFy7MjRs3MvyeKyP3UWpilDoJCUBERESGrx1SJh+ZM2cOzs7O9OzZk5UrV9K/f/8M3YsODg4YGRnpTCYD0LZtW5YsWfJWcWQ1eWImhBBCCPEGH3rs8PueLyYmhgcPHgApH3DeuHGDiRMnkjdvXqpVq4aZmRnt27dn5syZWFhYULFiRY4ePcrUqVPp0aMHkNJtb/LkyVhYWFCpUiXu3btHaGgolStXBlDWH7tw4UKa7olvo2bNmpQuXZqBAwcyevRoChQowNq1a9m4caMyTi4jPDw8cHJyYsiQIYwePZrcuXOzatUqrly5wrhx494ptsxgZmbGN998w+zZs8mTJw/FihVj48aNnDx5kipVqmSojtKlS+Pg4MD06dP54Ycf0i2Tuu7X2LFj6dKlC9HR0fj4+BAdHa10TTQ3NycyMpKbN2+SO3duDA0N2blzJ7a2tjx9+pQFCxbw4MEDEhISMhSXlZUVbdq0Yfbs2VhZWaV7H5UsWRILCwvmz5/PkCFDiIyMZMaMGW+V2BsaGjJ37lwsLS2pX78+T58+5a+//lK6babei5cuXUoz9s7MzIzOnTsza9YsbG1tKVGihNKdN3Vc3MdCEjMhhBBCiNdI1mr1Mo78fcYqL126VElqDAwMsLGxoWLFivj7+yvDAkaNGoWtrS2zZ88mMjISOzs7+vbtS8+ePQH46quvePbsGfPmzePu3btYW1vTqFEjhg4dCqS84a5duzYDBw5k8ODB6c6GlxFqtZqlS5cydepUBg0aRGxsLMWKFSMgIECZTCMjDA0NWbZsGZMnT6Zfv34kJCTg7OzM/PnzcXV1fafYMsuAAQNITExk9OjRxMbGUrduXerXr//aMWv/1aRJE+bPn//KGTDLly/PkiVLmDVrFq1bt8bMzIyqVasyYsQIpUthq1atCA4OplmzZgQHBzNp0iQCAgL49ddfyZMnD3Xq1KFr167s2bMnw0/NhgwZQt68eV95H1laWuLv78+0adPw9PSkSJEijBo16pUTfaTHw8MDX19fli5dyowZMzA1NaV27dqMHDkSABsbG9q0acOUKVOIiIigYcOGOscPHjwYQ0NDxo8fT1RUFE5OTixatEivT1LTo9JK/7D3FhYWRkJCAqVLl37n1evFq8XExHD+/Hlp3ywi7Zu1pH2znrRx1vpc2jcuLk4Zz/ymsUyZKTk5mYSEBIyNjVGr1R/svJ+L5ORk4uLiMDU11Wv7BgcHU7FiRZ2xXt26dcPOzo6JEyfqLa739bG074f2pt8Xqd0my5Yt+1b1yhgzIYQQQgg9Sp1AQWRfgYGBDBkyhPPnz3Pz5k2WL1/Ov//+m2a9LvF5k8RMCCGEEEKILOTv74+FhQVdu3alWbNmbNu2jVmzZlG1alV9hyY+IjLGTAghhBBCiCxkb2/PnDlz9B2G+MjJEzMhhBBCCCGE0DNJzIQQQggh/kfmRBNCvElW/Z6QxEwIIYQQnz1Dw5TRHUlJSXqORAjxsUv9PZH6eyOzSGImhBBCiM+eWq1GrVYTFRWl71CEEB+5qKgo5XdGZpLJP4QQQgjx2VOpVOTNm5e7d+9iYmKChYUFqndc3PltJCcnK4sMf07rQH0o0r5Z63NrX61WS3R0NFFRUeTPnz/Tf0dIYiaEEEIIAVhbWxMbG8vDhw958ODBBzmnRqMhKSkJQ0NDDAykI1Nmk/bNWp9j+6pUKnLmzIm1tXWm1y2JmRBCCCEEKW+48ufPT968eUlMTPwg54yNjeXq1asULlwYMzOzD3LOz4m0b9b6HNvXyMgoy54OSmImhBBCCPGSrBg78ioajQYAExMTTE1NP8g5PyfSvllL2jdzfR7PHIUQQgghhBDiIyaJmRBCCCGEEELomSRmQgghhBBCCKFnKq0scf/ejh8/jlarxcjI6INMrfu50Wq1JCYmSvtmEWnfrCXtm/WkjbOWtG/WkvbNWtK+WUvaN30JCQmoVCoqVKjwVsfJ5B+ZIPVGlBsya6hUKoyNjfUdRrYl7Zu1pH2znrRx1pL2zVrSvllL2jdrSfumT6VSvVNeIE/MhBBCCCGEEELPZIyZEEIIIYQQQuiZJGZCCCGEEEIIoWeSmAkhhBBCCCGEnkliJoQQQgghhBB6JomZEEIIIYQQQuiZJGZCCCGEEEIIoWeSmAkhhBBCCCGEnkliJoQQQgghhBB6JomZEEIIIYQQQuiZJGZCCCGEEEIIoWeSmAkhhBBCCCGEnkliJoQQQgghhBB6JonZe9BoNMyePZuaNWvi6upKt27diIiI0HdY2crt27dxcnJK87VhwwZ9h/ZJmzdvHt98843OtvPnz9O5c2fKly9PnTp1CAwM1FN02UN6bTxq1Kg093KtWrX0FOGn5enTp4wdO5ZatWpRoUIFOnTowNGjR5X9cv++vze1sdy/7+fRo0cMGzaMqlWr4ubmRs+ePbly5YqyX+7h9/Om9pX7N/Ncu3YNNzc3goKClG1y/2YOQ30H8CmbN28ea9euxc/Pj3z58jF16lR69OjB77//jrGxsb7DyxYuXryIiYkJu3fvRqVSKdutrKz0GNWnbfny5cyePZvKlSsr2548ecJ3331HgwYN8PHx4eTJk/j4+JAzZ07atGmjx2g/Tem1MaTcz7169aJz587KNrVa/aHD+yQNHjyYR48eMX36dGxtbVm9ejXff/89QUFB2Nrayv2bCV7XxsWKFZP79z317t0bAwMDFi9ejLm5ObNmzaJr164EBwcTFxcn9/B7el37mpmZyf2bSRITExk6dCgxMTHKNnkPkXkkMXtHCQkJLF26lGHDhlG7dm0AZsyYQc2aNQkODsbT01PPEWYPly5dokiRIuTNm1ffoXzy7t+/j7e3N8eOHaNIkSI6+9avX4+xsTHjx4/H0NCQYsWKERERweLFi+WX6lt4XRsnJydz5coVvLy8yJMnj54i/DRFRERw8OBB1qxZQ4UKFQDw9vZm3759/P7775iamsr9+57e1MZ9+/aV+/c9PHnyBHt7e3r37k2JEiUA8PLyomXLlly+fJlDhw7JPfwe3tS+ZcqUkfs3kwQEBGBhYaGzTd5DZB7pyviOLly4QHR0NFWrVlW25ciRA2dnZ44cOaLHyLKXixcvUrx4cX2HkS2cPXsWa2trtm7diqurq86+o0ePUrlyZQwN//+zmqpVq3Lt2jUePXr0oUP9ZL2uja9fv058fDzFihXTU3SfLhsbGxYtWoSLi4uyTaVSodVqefbsmdy/meBNbSz37/uxsbFh+vTpStLw8OFDAgMDsbOzo3jx4nIPv6c3ta/cv5njyJEjrFu3jsmTJ+tsl/s388gTs3d07949APLnz6+zPW/evNy9e1cfIWVLly5dIk+ePHTs2JHr16/j4OCAl5cXNWvW1Hdon5x69epRr169dPfdu3ePkiVL6mxLfUp5584dcuXKleXxZQeva+NLly6hUqlYsWIF+/btw8DAgNq1azNw4EDpmvsGOXLkUHompNq5cyc3btygRo0azJgxQ+7f9/SmNpb7N/OMGTNGecIwf/58zM3N5XdwJkqvfeX+fX9RUVEMHz6c0aNHp3nvK/dv5pEnZu8oNjYWIM1YMhMTE+Lj4/URUraTkJDA9evXefHiBQMHDmTRokWULVuWHj16cOjQIX2Hl63ExcWley8Dcj9nksuXL2NgYEDBggVZsGABI0aMICQkBC8vLzQajb7D+6QcO3aMH3/8kfr161OvXj25f7PAf9tY7t/M8+2337Jx40ZatGhBnz59OHv2rNzDmSi99pX79/2NHz+e8uXL07x58zT75P7NPPLE7B2ZmpoCKclD6veQcgOamZnpK6xsxdjYmCNHjmBoaKj8h3dxcSE8PJzAwECqVaum5wizD1NTUxISEnS2pf4yNTc310dI2U6/fv3o2rUrOXLkAKBkyZLkyZOHr7/+mrCwsDRdH0X6du/ezdChQ3F1dWX69OmA3L+ZLb02lvs386R2z//55585efIkv/zyi9zDmSi99p04caLcv+9h8+bNHD16lG3btqW7X+7fzCNPzN5R6mPcyMhIne2RkZHY2dnpI6RsydzcPM2nMCVLluT+/ft6iih7srOzS/deBsiXL58+Qsp2VCqV8qYgVWrXj9Su0eL1fvnlF/r160etWrVYvHix8qGY3L+Z51VtLPfv+3n06BG///47ycnJyjYDAwOKFSumvG+Qe/jdval95f59Pxs3buTRo0fUqVMHNzc33NzcABg3bhyenp5y/2YiSczeUalSpbC0tOTw4cPKtqioKM6dO0elSpX0GFn2ceHCBdzc3HTW0QE4c+aMTAiSySpXrsyxY8d0/qgdOnSIIkWKSN/wTDJkyBC+//57nW1hYWEAcj9nwOrVq/n555/p1KkTM2fO1PnARu7fzPG6Npb79/1ERkYyZMgQQkNDlW2JiYmcO3eOYsWKyT38nt7UvnL/vh9/f3927NjB5s2blS+A/v37s2jRIrl/M5EkZu/I2NiYzp074+/vz549e7hw4QKDBg3Czs6Ohg0b6ju8bKFkyZKUKFECHx8fjh49Snh4OH5+fpw8eZJevXrpO7xspU2bNrx48QJvb2+uXLlCUFAQK1as4IcfftB3aNlGs2bNOHjwIPPnz+fGjRuEhITw448/0qxZM5kp7A2uXbvGxIkTadiwIT/88AOPHj3iwYMHPHjwgOfPn8v9mwne1MZy/76fUqVKUaNGDeXv2aVLlxgxYgRRUVF07dpV7uH39Kb2lfv3/eTLlw8HBwedL4BcuXJRsGBBuX8zkUqr1Wr1HcSnKjk5menTpxMUFERcXByVK1dm7Nix2Nvb6zu0bOPx48f4+/uzb98+oqKicHZ2ZujQofJU8j2NHDmS27dvs2rVKmXb6dOn8fX15dy5c+TJk4du3brpLMQp3k56bfzHH3+wYMECrl69ipWVFc2bN2fgwIHKIGmRvgULFjBjxox093355ZdMmjRJ7t/3lJE2lvv3/Tx//pxp06axe/dunj9/TqVKlRg5cqQyxbvcw+/nTe0r92/mcnJyws/Pj9atWwNy/2YWScyEEEIIIYQQQs+kK6MQQgghhBBC6JkkZkIIIYQQQgihZ5KYCSGEEEIIIYSeSWImhBBCCCGEEHomiZkQQgghhBBC6JkkZkIIIYQQQgihZ5KYCSGEEK8gK8oIIYT4UAz1HYAQQohP3zfffENoaKjONiMjI3Lnzk3dunUZOHAg1tbWeoru3cyfPx8jIyO6d++u71AUTk5O9O3bl379+uk7FCGEEJlMEjMhhBCZwtnZmXHjximvExMTOXv2LNOnT+f8+fOsWbMGlUqlxwjfzsyZM+nbt6++wxBCCPGZkMRMCCFEprC0tKR8+fI62ypXrkx0dDSzZ8/m1KlTafYLIYQQIoWMMRNCCJGlXFxcALhz546ybffu3bRu3ZqyZcvi4eHBhAkTiImJUfYHBATQsGFD5syZg7u7Ow0aNODJkydotVp+/fVXPD09KVeuHA0bNmTx4sU6Y8GOHj1K586dcXV1pUqVKowYMYLHjx8r+4OCgnB2dubUqVN8/fXXlC1bljp16rB48WKljJOTEwBz5sxRvk+Nu2PHjri5ueHi4kLjxo355ZdfdK43PDycHj16UKFCBapXr86MGTMYNWoU33zzjVJGo9GwaNEiGjZsiIuLC40aNWLVqlXv29QAJCcn8+uvv9K8eXPKlStHnTp18Pf3Jz4+HoCJEydSpUoVNBqNcsyYMWNwcnLi6tWryrbVq1dTrlw5YmNjgYy364YNG6hRowa1atXi8uXLmXJNQgjxOZDETAghRJa6du0aAIUKFQJg27Zt9OnTh6JFizJ37lz69u3L1q1b8fLy0kmw7ty5Q3BwMNOnT2fgwIHY2Ngwffp0fH19qV27NvPnz6ddu3bMmDGDefPmAXDkyBG6du2KqakpM2fO5McffyQ0NJQuXboQFxen1K3RaBg4cCBNmzZl0aJFVKxYEX9/f/bv3w/AunXrAGjbtq3y/d9//02fPn0oU6YM8+bNIyAggIIFC/Lzzz9z/PhxAB4/fkznzp25e/cufn5+jB49ml27dvH777/rtMn48eOZPXs2LVq0YMGCBTRu3JiJEycyd+7c927vsWPHMnHiROrVq8f8+fPp1KkTv/zyi9K+devW5dmzZ5w5c0Y55t9//1XaL9W+ffuoVq0aZmZmGW7X5ORkFixYwIQJExg4cCDFixd/7+sRQojPhXRlFEIIkSm0Wi1JSUnK62fPnhEaGsr8+fMpX748Li4uaLVa/P39qVmzJv7+/kpZR0dHunbtSkhICHXq1AEgKSmJESNGUL16dQCioqJYtmwZ33zzDcOHDwfAw8ODx48fc+zYMQCmTZtGkSJFWLhwIWq1GgBXV1c8PT3ZuHEjnTp1UmL18vKiXbt2AFSsWJHg4GD+/vtvatasqXS5tLOzU76/cuUKrVq1wtvbW4nbzc0Nd3d3jhw5QoUKFVi1ahXR0dFs3ryZfPnyKedv1KiRcsy1a9dYv349gwcPpmfPngDUqFEDlUrFwoUL6dixIzY2Nu/0M7hy5Qq//fYbAwcOpHfv3kob5c2bl+HDh7Nv3z6qV6+OhYUFhw4doly5cty+fZsbN25QpkwZQkND+frrr0lISODw4cOMGDHirdoVoFevXsrPUAghRMbJEzMhhBCZ4siRI5QpU0b5ql69OoMHD6ZMmTJMnz4dlUrF1atXuXfvHvXq1SMpKUn5qly5MpaWlhw8eFCnzpIlSyrfnzx5ksTERBo2bKhTZuTIkSxdupTY2FhOnTpF7dq1lSQxKSmJQoUKUaxYsTR1u7m5Kd8bGxtja2ur053yv7p3787kyZOJiYnhwoUL7Ny5k0WLFgEpE51AypMnNzc3JSkDKFiwoM65/v33X7RabZo2qFevHvHx8UqS+S5SZ8Zs3ry5znZPT0/UajWHDx/GyMgIDw8P/vnnHwAOHTqEg4MDTZo0UY4/cuQIMTEx1K1b963b9eWfmRBCiIyTJ2ZCCCEyRZkyZfDx8QFApVJhYmJC/vz5sbS0VMo8ffoUAB8fH6XsyyIjI3Ve586dO82xtra26Z4/KioKjUbD4sWLdcaLpTIxMdF5bWpqqvPawMDgteuWPX78mHHjxrF7925UKhUODg5UrFgR+P/1zh4/fkyZMmXSHJsnTx4ePHigcx2enp7pnuf+/fuvjOFNnj17ppzvZYaGhtjY2PD8+XMAateujY+PD3FxcRw6dAh3d3fc3d3x9/fn+vXr7Nu3jzJlypAvXz7u37//Vu2aK1eud45fCCE+Z5KYCSGEyBQWFhaULVv2tWVy5MgBwPDhw6lSpUqa/a9b6yz12MePH1O0aFFl+927d4mIiMDFxQWVSkXXrl3TTXrMzMwydB2vMnToUMLDw1m2bBkVKlTA2NiY2NhYNmzYoJSxs7Pj0aNHaY59eVvqdaxYsQILC4s0ZQsUKPDOMaa234MHD7C3t1e2JyYm8uTJE6WLZO3atUlMTOTYsWNKl8UyZcpgaWnJkSNH2LdvH02bNgVSfq5Z2a5CCCFSSFdGIYQQH0zRokXJlSsXt27domzZssqXnZ0d06ZN49y5c688tly5chgZGbFnzx6d7StWrGDAgAGYmpri7OzM1atXdeouUaIEc+bM4fDhw28Vq4GB7p/IY8eO0ahRI6pWrYqxsTGQMkEGoMxwWLlyZU6cOKE8HYOUJOnkyZPK68qVKwPw5MkTnTifPn3KzJkzlSdq7yI12d22bZvO9u3bt5OcnKw84cuTJw/Ozs6sWbOGBw8eUKVKFdRqNZUrV2bTpk1cvXqVunXrAinLIGRmuwohhEifPDETQgjxwajVagYNGsTYsWNRq9XUrVuXqKgo5s2bx/3799PtBpjK1taWLl26sGLFCoyNjalatSphYWH88ssvDB48GENDQ2VCjSFDhtCiRQuSk5NZunQpp06dUibDyKgcOXJw4sQJjhw5QqVKlShXrhzbtm2jTJky2NnZceLECRYuXIhKpVKmlO/SpQu//vor33//PX369AFg7ty5JCQkKItrlyxZkhYtWjBmzBhu376Ni4sL165dY8aMGdjb2+Po6PjauE6ePMny5cvTbK9RowbFixfnyy+/ZM6cOcTFxeHu7s758+eVZQdq1qyplK9Tpw5z586lSJEiypg4d3d3Jk2aRN68eXV+FpnZrkIIIdIniZkQQogPql27dlhYWLBkyRLWrVuHubk5FSpUwN/fX5lS/1WGDRtG7ty5WbNmDUuXLsXe3p4ff/yRjh07AinJSWBgIHPmzKF///4YGRlRpkwZli1b9taLW/fq1Yt58+bRo0cPduzYwaRJk/j555/5+eefgZSZJH18fNi6dStHjx4FUpK5lStX4uvry/Dhw7GwsKBjx46Ym5tjbm6u1O3n58fChQtZu3Yt9+7dI1euXDRt2pSBAwcqsx6+yoEDBzhw4ECa7X5+fhQvXhxfX18cHBzYuHEjgYGB5M2bl2+++YY+ffroPAVMTcxe7lLq7u6u7EtNJDO7XYUQQqRPpX3dSGchhBBCZNipU6d4+vQptWvXVrYlJSVRp04dPD09GTVqlB6jE0II8TGTJ2ZCCCFEJrlz5w6DBg2iT58+VKlShdjYWNauXcvz58/56quv9B2eEEKIj5g8MRNCCCEy0Zo1a1i9ejU3b97EyMgIV1dXBgwY8MYZK4UQQnzeJDETQgghhBBCCD2T6fKFEEIIIYQQQs8kMRNCCCGEEEIIPZPETAghhBBCCCH0TBIzIYQQQgghhNAzScyEEEIIIYQQQs8kMRNCCCGEEEIIPZPETAghhBBCCCH0TBIzIYQQQgghhNAzScyEEEIIIYQQQs/+D0X9b6dbWw8wAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "bar_width = 0.25\n",
    "\n",
    "index = np.arange(len(energies_df))\n",
    "\n",
    "bars3 = ax.barh(index, energies_df[\"Slowest meta heuristic is lower in %\"], color='lightgreen', height=bar_width, label=\"Worst Performing Metaheuristic\")\n",
    "bars1 = ax.barh(index + bar_width, energies_df[\"Average meta heuristic is lower in %\"], color='salmon', height=bar_width, label=\"Average Metaheuristic\")\n",
    "bars2 = ax.barh(index + 2*bar_width, energies_df[\"Fastest meta heuristic is lower in %\"], color='turquoise', height=bar_width, label=\"Best Performing Metaheuristic\")\n",
    "\n",
    "\n",
    "ax.set_yticks(index + bar_width)\n",
    "ax.set_yticklabels(energies_df[\"No of Tasks\"])  \n",
    "ax.set_xlabel(\"Percentage Lower\")\n",
    "ax.set_ylabel(\"Number of Tasks\")\n",
    "ax.set_title(\"Decrease in energy from the average of traditional allocation\")\n",
    "\n",
    "ax.legend()\n",
    "\n",
    "ax.invert_yaxis()\n",
    "\n",
    "for bar in bars1:\n",
    "    width = bar.get_width()\n",
    "    ax.text(width - 1.5, bar.get_y() + bar.get_height() / 2, f'{width:.2f}%', \n",
    "            ha='center', va='center', color='black', fontsize=10)\n",
    "\n",
    "for bar, algo in zip(bars2, costs_df[\"Best allocation\"]):\n",
    "    width = bar.get_width()\n",
    "    ax.text(width - 1.5, bar.get_y() + bar.get_height() / 2, f'{width:.2f}%', \n",
    "            ha='center', va='center', color='black', fontsize=10)\n",
    "    ax.text(width + 0.1, bar.get_y() + bar.get_height() / 2, algo, \n",
    "            ha='left', va='center', color='black', fontsize=10)\n",
    "\n",
    "for bar, algo in zip(bars3, costs_df[\"Worst allocation\"]):\n",
    "    width = bar.get_width()\n",
    "    ax.text(width - 1.5, bar.get_y() + bar.get_height() / 2, f'{width:.2f}%', \n",
    "            ha='center', va='center', color='black', fontsize=10)\n",
    "    ax.text(width + 0.1, bar.get_y() + bar.get_height() / 2, algo, \n",
    "            ha='left', va='center', color='black', fontsize=10)\n",
    "\n",
    "plt.savefig(\"energies_decrease_perc_task.png\", bbox_inches='tight') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65e92fa1-1c7d-48ac-8d4d-d262497cff44",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "0cb88603-8f23-4cf5-85aa-282f36f38526",
   "metadata": {},
   "source": [
    "# MAKESPAN COMPARISON"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "id": "ce8ec839-a6f1-4363-aae4-f881d740f643",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_makespan=pd.read_excel(\"makespan vs computing.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "id": "cef14793-0f1e-4dce-8f43-adde27cc8e63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>max task CPU</th>\n",
       "      <th>max task memory</th>\n",
       "      <th>RR</th>\n",
       "      <th>SJF</th>\n",
       "      <th>FWA</th>\n",
       "      <th>SQSA</th>\n",
       "      <th>BAT</th>\n",
       "      <th>PSO</th>\n",
       "      <th>BMO</th>\n",
       "      <th>SSA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>85.490122</td>\n",
       "      <td>84.712605</td>\n",
       "      <td>64.153695</td>\n",
       "      <td>60.737885</td>\n",
       "      <td>70.179464</td>\n",
       "      <td>70.136827</td>\n",
       "      <td>67.990053</td>\n",
       "      <td>67.547998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>152.441602</td>\n",
       "      <td>146.423700</td>\n",
       "      <td>105.128693</td>\n",
       "      <td>110.324668</td>\n",
       "      <td>119.189744</td>\n",
       "      <td>125.975898</td>\n",
       "      <td>120.808843</td>\n",
       "      <td>109.755718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>201.181418</td>\n",
       "      <td>206.954041</td>\n",
       "      <td>156.689402</td>\n",
       "      <td>146.590138</td>\n",
       "      <td>161.141215</td>\n",
       "      <td>174.944137</td>\n",
       "      <td>169.800495</td>\n",
       "      <td>161.308764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>250</td>\n",
       "      <td>250</td>\n",
       "      <td>249.285277</td>\n",
       "      <td>253.365512</td>\n",
       "      <td>176.883314</td>\n",
       "      <td>171.879438</td>\n",
       "      <td>184.671580</td>\n",
       "      <td>211.003155</td>\n",
       "      <td>189.368258</td>\n",
       "      <td>183.500985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>300</td>\n",
       "      <td>300</td>\n",
       "      <td>327.037005</td>\n",
       "      <td>325.801085</td>\n",
       "      <td>228.848153</td>\n",
       "      <td>220.253475</td>\n",
       "      <td>264.054259</td>\n",
       "      <td>276.591812</td>\n",
       "      <td>252.501458</td>\n",
       "      <td>250.740588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>350</td>\n",
       "      <td>350</td>\n",
       "      <td>381.424806</td>\n",
       "      <td>389.457666</td>\n",
       "      <td>290.779569</td>\n",
       "      <td>271.066942</td>\n",
       "      <td>335.313309</td>\n",
       "      <td>337.059332</td>\n",
       "      <td>306.361796</td>\n",
       "      <td>307.925110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   max task CPU  max task memory          RR         SJF         FWA  \\\n",
       "0           100              100   85.490122   84.712605   64.153695   \n",
       "1           150              150  152.441602  146.423700  105.128693   \n",
       "2           200              200  201.181418  206.954041  156.689402   \n",
       "3           250              250  249.285277  253.365512  176.883314   \n",
       "4           300              300  327.037005  325.801085  228.848153   \n",
       "5           350              350  381.424806  389.457666  290.779569   \n",
       "\n",
       "         SQSA         BAT         PSO         BMO         SSA  \n",
       "0   60.737885   70.179464   70.136827   67.990053   67.547998  \n",
       "1  110.324668  119.189744  125.975898  120.808843  109.755718  \n",
       "2  146.590138  161.141215  174.944137  169.800495  161.308764  \n",
       "3  171.879438  184.671580  211.003155  189.368258  183.500985  \n",
       "4  220.253475  264.054259  276.591812  252.501458  250.740588  \n",
       "5  271.066942  335.313309  337.059332  306.361796  307.925110  "
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_makespan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "id": "fda1393b-042a-4932-9285-18f684a54eb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "makespans=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "id": "d8955cae-3ded-43db-9566-2625c7033e28",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max CPU:  100\n",
      "[(60.737885, 'SQSA'), (64.153695, 'FWA'), (67.547998, 'SSA'), (67.990053, 'BMO'), (70.136827, 'PSO'), (70.179464, 'BAT'), (84.712605, 'SJF'), (85.490122, 'RR')]\n",
      "Average Traditional:  85.10136349999999\n",
      "Average Metaheuristic:  66.790987\n",
      "Slowest meta heuristic is lower in %:  17.53\n",
      "Average meta heuristic is lower in %:  21.52\n",
      "Fastest meta heuristic is lower in %:  28.63\n",
      "Best allocation algorithm:  (60.737885, 'SQSA')\n",
      "Worst allocation algorithm:  (70.179464, 'BAT')\n",
      "\n",
      "Max CPU:  150\n",
      "[(105.128693, 'FWA'), (109.755718, 'SSA'), (110.324668, 'SQSA'), (119.189744, 'BAT'), (120.808843, 'BMO'), (125.975898, 'PSO'), (146.4237, 'SJF'), (152.441602, 'RR')]\n",
      "Average Traditional:  149.432651\n",
      "Average Metaheuristic:  115.19726066666668\n",
      "Slowest meta heuristic is lower in %:  15.7\n",
      "Average meta heuristic is lower in %:  22.91\n",
      "Fastest meta heuristic is lower in %:  29.65\n",
      "Best allocation algorithm:  (105.128693, 'FWA')\n",
      "Worst allocation algorithm:  (125.975898, 'PSO')\n",
      "\n",
      "Max CPU:  200\n",
      "[(146.590138, 'SQSA'), (156.689402, 'FWA'), (161.141215, 'BAT'), (161.308764, 'SSA'), (169.800495, 'BMO'), (174.944137, 'PSO'), (201.181418, 'RR'), (206.954041, 'SJF')]\n",
      "Average Traditional:  204.06772949999998\n",
      "Average Metaheuristic:  161.74569183333332\n",
      "Slowest meta heuristic is lower in %:  14.27\n",
      "Average meta heuristic is lower in %:  20.74\n",
      "Fastest meta heuristic is lower in %:  28.17\n",
      "Best allocation algorithm:  (146.590138, 'SQSA')\n",
      "Worst allocation algorithm:  (174.944137, 'PSO')\n",
      "\n",
      "Max CPU:  250\n",
      "[(171.879438, 'SQSA'), (176.883314, 'FWA'), (183.500985, 'SSA'), (184.67158, 'BAT'), (189.368258, 'BMO'), (211.003155, 'PSO'), (249.285277, 'RR'), (253.365512, 'SJF')]\n",
      "Average Traditional:  251.32539450000002\n",
      "Average Metaheuristic:  186.2177883333333\n",
      "Slowest meta heuristic is lower in %:  16.04\n",
      "Average meta heuristic is lower in %:  25.91\n",
      "Fastest meta heuristic is lower in %:  31.61\n",
      "Best allocation algorithm:  (171.879438, 'SQSA')\n",
      "Worst allocation algorithm:  (211.003155, 'PSO')\n",
      "\n",
      "Max CPU:  300\n",
      "[(220.253475, 'SQSA'), (228.848153, 'FWA'), (250.740588, 'SSA'), (252.501458, 'BMO'), (264.054259, 'BAT'), (276.591812, 'PSO'), (325.801085, 'SJF'), (327.037005, 'RR')]\n",
      "Average Traditional:  326.419045\n",
      "Average Metaheuristic:  248.83162416666664\n",
      "Slowest meta heuristic is lower in %:  15.26\n",
      "Average meta heuristic is lower in %:  23.77\n",
      "Fastest meta heuristic is lower in %:  32.52\n",
      "Best allocation algorithm:  (220.253475, 'SQSA')\n",
      "Worst allocation algorithm:  (276.591812, 'PSO')\n",
      "\n",
      "Max CPU:  350\n",
      "[(271.066942, 'SQSA'), (290.779569, 'FWA'), (306.361796, 'BMO'), (307.92511, 'SSA'), (335.313309, 'BAT'), (337.059332, 'PSO'), (381.424806, 'RR'), (389.457666, 'SJF')]\n",
      "Average Traditional:  385.441236\n",
      "Average Metaheuristic:  308.08434300000005\n",
      "Slowest meta heuristic is lower in %:  12.55\n",
      "Average meta heuristic is lower in %:  20.07\n",
      "Fastest meta heuristic is lower in %:  29.67\n",
      "Best allocation algorithm:  (271.066942, 'SQSA')\n",
      "Worst allocation algorithm:  (337.059332, 'PSO')\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(df_makespan)):\n",
    "    row=df_makespan.iloc[i]\n",
    "    row_tuple=[(row[col],col) for col in df_makespan.columns]\n",
    "    \n",
    "    cpu=row_tuple[0][0]\n",
    "    memory=row_tuple[1][0]\n",
    "    print(\"Max CPU: \",int(cpu))\n",
    "    \n",
    "    row_tuple=row_tuple[2:]\n",
    "    row_tuple.sort()\n",
    "    print(row_tuple)\n",
    "    \n",
    "    average_traditional=(row_tuple[-1][0]+row_tuple[-2][0])/2\n",
    "    print(\"Average Traditional: \",average_traditional)\n",
    "    \n",
    "    average_meta=0\n",
    "    for j in range(6):\n",
    "        average_meta+=row_tuple[j][0]\n",
    "    average_meta/=6\n",
    "    \n",
    "    print(\"Average Metaheuristic: \",average_meta)\n",
    "    \n",
    "    slowest_percentage_meta_lower=(average_traditional-row_tuple[-3][0])/average_traditional*100\n",
    "    print(\"Slowest meta heuristic is lower in %: \", round(slowest_percentage_meta_lower,2))\n",
    "    \n",
    "    average_percentage_meta_lower=(average_traditional-average_meta)/average_traditional*100\n",
    "    print(\"Average meta heuristic is lower in %: \", round(average_percentage_meta_lower,2))\n",
    "    \n",
    "    fastest_percentage_meta_lower=(average_traditional-row_tuple[0][0])/average_traditional*100\n",
    "    print(\"Fastest meta heuristic is lower in %: \", round(fastest_percentage_meta_lower,2))\n",
    "    \n",
    "    print(\"Best allocation algorithm: \", row_tuple[0])\n",
    "    print(\"Worst allocation algorithm: \", row_tuple[-3])\n",
    "    makespans.append((cpu,memory,average_traditional,average_meta,slowest_percentage_meta_lower,average_percentage_meta_lower,fastest_percentage_meta_lower,row_tuple[0][1],row_tuple[-3][1]))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "id": "8a118df9-9ce9-4131-adea-5b11b39dcd31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Max CPU</th>\n",
       "      <th>Max Memory</th>\n",
       "      <th>Average Traditional</th>\n",
       "      <th>Average Metaheuristic</th>\n",
       "      <th>Slowest meta heuristic is lower in %</th>\n",
       "      <th>Average meta heuristic is lower in %</th>\n",
       "      <th>Fastest meta heuristic is lower in %</th>\n",
       "      <th>Best allocation</th>\n",
       "      <th>Worst allocation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>85.101363</td>\n",
       "      <td>66.790987</td>\n",
       "      <td>17.534266</td>\n",
       "      <td>21.515961</td>\n",
       "      <td>28.628776</td>\n",
       "      <td>SQSA</td>\n",
       "      <td>BAT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>150.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>149.432651</td>\n",
       "      <td>115.197261</td>\n",
       "      <td>15.697207</td>\n",
       "      <td>22.910248</td>\n",
       "      <td>29.648111</td>\n",
       "      <td>FWA</td>\n",
       "      <td>PSO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>200.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>204.067729</td>\n",
       "      <td>161.745692</td>\n",
       "      <td>14.271533</td>\n",
       "      <td>20.739211</td>\n",
       "      <td>28.165939</td>\n",
       "      <td>SQSA</td>\n",
       "      <td>PSO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>250.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>251.325395</td>\n",
       "      <td>186.217788</td>\n",
       "      <td>16.043838</td>\n",
       "      <td>25.905701</td>\n",
       "      <td>31.610796</td>\n",
       "      <td>SQSA</td>\n",
       "      <td>PSO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>300.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>326.419045</td>\n",
       "      <td>248.831624</td>\n",
       "      <td>15.264806</td>\n",
       "      <td>23.769269</td>\n",
       "      <td>32.524319</td>\n",
       "      <td>SQSA</td>\n",
       "      <td>PSO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>350.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>385.441236</td>\n",
       "      <td>308.084343</td>\n",
       "      <td>12.552342</td>\n",
       "      <td>20.069698</td>\n",
       "      <td>29.673601</td>\n",
       "      <td>SQSA</td>\n",
       "      <td>PSO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Max CPU  Max Memory  Average Traditional  Average Metaheuristic  \\\n",
       "0    100.0       100.0            85.101363              66.790987   \n",
       "1    150.0       150.0           149.432651             115.197261   \n",
       "2    200.0       200.0           204.067729             161.745692   \n",
       "3    250.0       250.0           251.325395             186.217788   \n",
       "4    300.0       300.0           326.419045             248.831624   \n",
       "5    350.0       350.0           385.441236             308.084343   \n",
       "\n",
       "   Slowest meta heuristic is lower in %  Average meta heuristic is lower in %  \\\n",
       "0                             17.534266                             21.515961   \n",
       "1                             15.697207                             22.910248   \n",
       "2                             14.271533                             20.739211   \n",
       "3                             16.043838                             25.905701   \n",
       "4                             15.264806                             23.769269   \n",
       "5                             12.552342                             20.069698   \n",
       "\n",
       "   Fastest meta heuristic is lower in % Best allocation Worst allocation  \n",
       "0                             28.628776            SQSA              BAT  \n",
       "1                             29.648111             FWA              PSO  \n",
       "2                             28.165939            SQSA              PSO  \n",
       "3                             31.610796            SQSA              PSO  \n",
       "4                             32.524319            SQSA              PSO  \n",
       "5                             29.673601            SQSA              PSO  "
      ]
     },
     "execution_count": 322,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "makespans_df=pd.DataFrame(makespans,columns=[\"Max CPU\",\"Max Memory\",\"Average Traditional\",\"Average Metaheuristic\",\"Slowest meta heuristic is lower in %\",\n",
    "                            \"Average meta heuristic is lower in %\", \"Fastest meta heuristic is lower in %\", \n",
    "                            \"Best allocation\",\"Worst allocation\"])\n",
    "makespans_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "id": "a22dcce0-d12d-490d-ae22-fef2aa94b973",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "bar_width = 0.25\n",
    "\n",
    "index = np.arange(len(makespans_df))\n",
    "\n",
    "bars3 = ax.barh(index, makespans_df[\"Slowest meta heuristic is lower in %\"], color='lightgreen', height=bar_width, label=\"Worst Performing Metaheuristic\")\n",
    "bars1 = ax.barh(index + bar_width, makespans_df[\"Average meta heuristic is lower in %\"], color='salmon', height=bar_width, label=\"Average Metaheuristic\")\n",
    "bars2 = ax.barh(index + 2*bar_width, makespans_df[\"Fastest meta heuristic is lower in %\"], color='turquoise', height=bar_width, label=\"Best Performing Metaheuristic\")\n",
    "\n",
    "\n",
    "ax.set_yticks(index + bar_width)\n",
    "ax.set_yticklabels(makespans_df[\"Max CPU\"])  \n",
    "ax.set_xlabel(\"Percentage Lower\")\n",
    "ax.set_ylabel(\"Max CPU and Max Memory (each)\")\n",
    "ax.set_title(\"Decrease in makespan from the average of traditional allocation\")\n",
    "ax.set_xlim(0,45)\n",
    "\n",
    "ax.legend(fontsize='small', loc='center right')\n",
    "\n",
    "ax.invert_yaxis()\n",
    "\n",
    "for bar in bars1:\n",
    "    width = bar.get_width()\n",
    "    ax.text(width - 1.5, bar.get_y() + bar.get_height() / 2, f'{width:.2f}%', \n",
    "            ha='center', va='center', color='black', fontsize=10)\n",
    "\n",
    "for bar, algo in zip(bars2, costs_df[\"Best allocation\"]):\n",
    "    width = bar.get_width()\n",
    "    ax.text(width - 1.5, bar.get_y() + bar.get_height() / 2, f'{width:.2f}%', \n",
    "            ha='center', va='center', color='black', fontsize=10)\n",
    "    ax.text(width + 0.1, bar.get_y() + bar.get_height() / 2, algo, \n",
    "            ha='left', va='center', color='black', fontsize=10)\n",
    "\n",
    "for bar, algo in zip(bars3, costs_df[\"Worst allocation\"]):\n",
    "    width = bar.get_width()\n",
    "    ax.text(width - 1.5, bar.get_y() + bar.get_height() / 2, f'{width:.2f}%', \n",
    "            ha='center', va='center', color='black', fontsize=10)\n",
    "    ax.text(width + 0.1, bar.get_y() + bar.get_height() / 2, algo, \n",
    "            ha='left', va='center', color='black', fontsize=10)\n",
    "\n",
    "plt.savefig(\"makespan_decrease_perc.png\", bbox_inches='tight') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7ab14ae-004a-4640-840c-78177d87be68",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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